Period 1
library(dplyr)
library(igraph)
library(tidygraph)
library(sf)
library(ggplot2)
library(sp)
library(STNet)
library(raster)
library(fasterize)
library(here)
library(gridExtra)
library(nngeo)
library(scales)
library(cowplot)Objectives:
- Predict the expected number and location of outbreaks detected in farms and wild boars in the next four weeks.
- Predict the effectiveness of fencing infected zones.
- Advise regarding implementation of the increased hunting pressure in the fenced area.
Deadline: October 8th, 2020.
Data overview:
# Get the CRS that we will use for the data
projCRS <- shapefile(here("Data/InitialData/Island_ADMIN.shp")) %>% crs()
# Resolution of the grid:
HxRes <- 15e3
# Read the shapefile
Is <- sf::read_sf(here("Data/InitialData/Island_ADMIN.shp"))
# explicitly indicate the CRS in sp format to avoid future issues when converting between formats
st_crs(Is) <- projCRS
# Convert to a field
Border <- as(raster::extent(Is), "SpatialPolygons") %>%
sf::st_as_sf()
st_crs(Border) <- st_crs(Is)
# Create the grid
new_hex_grid = function(cellsize, border, orig_shp) {
DFsp <- border %>% as("Spatial")
HexPts <- sp::spsample(DFsp, type = "hexagonal", offset = c(0,
0), cellsize = cellsize)
HexPols <- sp::HexPoints2SpatialPolygons(HexPts)
HexPols_sf = st_as_sf(HexPols)
hexgrid = st_join(hsf, orig_shp) %>%
filter(!is.na(ID)) %>%
rename(orig_shp_ID = ID) %>%
mutate(idhex = as.character(seq_len(nrow(.))))
return(hexgrid)
}
BHx <- new_hex_grid(cellsize = HxRes, border = Border, orig_shp = Is)
if(FALSE){
# Older version, which returns hexgrids for entire border extent,
# instead of just for the polygon area
BHx <- STNet::HexGrid(cellsize = HxRes, Shp = Border)
plot(BHx)
}
ggplot() +
geom_sf(data = border) +
geom_sf(data = BHx, aes(fill = orig_shp_ID, col = orig_shp_ID), alpha = 0.7) +
geom_sf(data = Is, aes(fill = ID), col = "grey35", alpha = 0.5) +
theme_void()
# Note: the ID's aren't sequential - they're like that from the Island data
BHx$orig_shp_ID = NULL # don't need the orig IDs going forwardHerds.
herds50 <- read.csv(here("Data/Period_1/herds_day_50.csv"))
######## Summarize herds ########
herds50 <- herds50 %>%
mutate(N=1) %>%
tidyr::spread(production, N) %>% # Convert from long to wide for farm types
replace(., is.na(.), 0) # Replace NAs with 0
herdsSp <- herds50 %>%
sf::st_as_sf(coords = c("X", "Y"), crs = st_crs(Is)) %>%
st_join(BHx)
herdsHx <- herdsSp %>%
mutate(N = 1) %>%
group_by(idhex) %>%
summarise_at(.vars = c('N', 'size', 'is_outdoor', 'is_commercial', 'multisite', 'B', 'BF', 'F'), .funs = sum) %>%
mutate(density = size/ N) %>% # expected animals per farm
data.frame() %>%
dplyr::select(-geometry) %>%
rename(Farms = N, Pop = size)
Hx <- BHx %>%
left_join(herdsHx)## Joining, by = "idhex"Movements
Mov <- read.csv(here("Data/Period_1/moves_Players_day_50.csv"))
MovHx <- Mov %>%
left_join(data.frame(herdsSp)[c('population_id', 'idhex')], by = c('source' = 'population_id')) %>%
rename(sourceHx = idhex) %>%
left_join(data.frame(herdsSp)[c('population_id', 'idhex')], by = c('dest' = 'population_id')) %>%
rename(destHx = idhex) %>%
mutate_at(.vars = c('sourceHx', 'destHx'), .funs = as.character)
# How long was the period observed?
period_t <- MovHx %>%
count(date) %>%
nrow()
# Get the probability of movements (long distance) and loops (short distance/internals)
MovpHx <- MovHx %>%
mutate(Mov = 1, loop = ifelse(sourceHx == destHx, 1, 0)) %>%
group_by(sourceHx) %>%
summarise(Mov = sum(Mov, ma.rm = T), loop = sum(loop), E_animals = median(qty)) %>%
mutate(Mov = Mov/period_t, loop = loop/period_t)
# join with the herds dataset
Hx <- Hx %>%
left_join(MovpHx, by = c('idhex' = 'sourceHx'))
MovHx %>%
mutate(ID = 1:n(), sourceHx = as.integer(sourceHx), destHx = as.integer(destHx)) %>%
dplyr::select(ID, sourceHx, destHx) %>%
# slice(1:200) %>%
write.csv(., here("Data/Period_1/out/MovHx.csv"), row.names = FALSE, quote = FALSE)wild boars
hunting <- read.csv(here("Data/InitialData/WB_HuntingBag.csv"), sep = ";")
Is <- Is %>%
mutate(ID = as.integer(as.character(ID)), area = st_area(.)) %>%
left_join(hunting, by = c("ID" = "ADM")) %>%
mutate(WB_dens = HB_2019/area)
# Create an empty raster (increaseing the ncol and nrow will give us a better resolution, but will also increase the computation time)
r <- raster(ncol = 1000, nrow = 1000)
crs(r) <- crs(Is)
# Set the extent same as the shapefile
extent(r) <- extent(Is)
WBr <- fasterize::fasterize(sf = Is, raster = r, field = "WB_dens", fun = "sum")
# Specify the CRS
crs(WBr) <- projCRS
Vals <- lapply(seq_len(nrow(Hx)), function(x){
mean(raster::extract(WBr, Hx[x,])[[1]], na.rm = TRUE)
})
Hx <- Hx %>%
mutate(WBd = do.call(rbind, Vals), E_WB = (WBd * HxRes) * 1000)
# Compare plots
Hx %>%
ggplot()+
geom_sf(aes(fill = E_WB))
plot(WBr)
Land cover
LC <- st_read(here("Data/Shapefiles/Island_LANDCOVER.shp"))## Reading layer `Island_LANDCOVER' from data source `/Users/nistara/Box Sync/ASF Shared/Data/Shapefiles/Island_LANDCOVER.shp' using driver `ESRI Shapefile'
## Simple feature collection with 54011 features and 2 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 428145.7 ymin: 6137116 xmax: 1027293 ymax: 6633766
## epsg (SRID): NA
## proj4string: +proj=lcc +lat_1=44 +lat_2=49 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +units=m +no_defs# Recode the land cover levels to manipulate later
LC <- LC %>%
mutate(LC = recode(LANDCOVER, Agricultural = 1, Forest = 10, Urban = 100))We will put all the information in a raster so we can then extract and summarize the values per hexagon
# Use the function fasterize to sum the number of N over each pixel of our raster
LCr <- fasterize::fasterize(sf = LC, raster = r, field = "LC", fun = "sum")
# Specify the CRS
crs(LCr) <- projCRS # NR: for me the crs' were already identical
# Get the unique values to see if worked
unique(values(LCr))## [1] NA 20 2 10 1 100 3 200 4 30 40Some of the values are not the ones we recoded for (there might be some overlapping of the values due to the resolution used), we can see that the unexpected numbers are just sums of two of the same type, so we will just replace those:
# Replace NAs for 0s
LCr[is.na(LCr[])] <- 0
# Replace overlapping raster cells
LCr[(LCr[] > 1 & LCr[] < 9)] <- 1
LCr[(LCr[] > 10 & LCr[] < 90)] <- 10
LCr[(LCr[] > 100 & LCr[] < 900)] <- 100
# Plot the raster
plot(LCr)
Now we will summarize the values per hexagon
# Updated this code with newer version below
if(FALSE){
# Create empty vectors
A_vals <- vector()
F_vals <- vector()
U_vals <- vector()
# Make a loop that will run for each hexagon
for(i in 1:nrow(Hx)){
# Get the values for each haxagon
Vals <- raster::extract(LCr, Hx[i,])
# get the proportion of each type of land cover
AVals_i <- length(Vals[[1]][Vals[[1]] == 1]) / length(Vals[[1]])
FVals_i <- length(Vals[[1]][Vals[[1]] == 10]) / length(Vals[[1]])
UVals_i <- length(Vals[[1]][Vals[[1]] == 100]) / length(Vals[[1]])
# create vectors with the values:
A_vals <- c(A_vals, AVals_i)
F_vals <- c(F_vals, FVals_i)
U_vals <- c(U_vals, UVals_i)
}
}
# Make a loop that will run for each hexagon
Vals_l = lapply(seq_len(nrow(Hx)), function(n, Hx, LCr) {
Vals <- raster::extract(LCr, Hx[n, ])[[1]]
}, Hx, LCr)
# get the proportion of each type of land cover
A_vals = sapply(Vals_l, function(Vals) {
sum(Vals == 1) / length(Vals)
})
F_vals = sapply(Vals_l, function(Vals) {
sum(Vals == 10) / length(Vals)
})
U_vals = sapply(Vals_l, function(Vals) {
sum(Vals == 100) / length(Vals)
})
# Add the values to the Spatial hexagon layer:
Hx <- Hx %>%
mutate(Agricultural = A_vals, Forest = F_vals, Urban = U_vals)
# Plots
p_urban = Hx %>%
ggplot()+
geom_sf(aes(fill = Urban)) +
scale_fill_gradient(limits = c(0, 1), high = "yellow")
p_forest = Hx %>%
ggplot()+
geom_sf(aes(fill = Forest)) +
scale_fill_gradient(limits = c(0, 1), high = "green")
p_agricultural = Hx %>%
ggplot()+
geom_sf(aes(fill = Agricultural)) +
scale_fill_gradient(limits = c(0, 1), high = "purple")
gridExtra::grid.arrange(p_urban, p_forest, p_agricultural, ncol = 3)
Cases
Cases comes in two datasets, one for positives and other for negative tests.
# Variable to spread categorical variables
unfold <- function(Dat, Var){
Dat %>% mutate(N=1) %>% tidyr::spread(eval(parse(text = Var)), N, fill = 0)
}# For some reason the original st_join method was giving two hex joins for some points
# Possibly due to points being on the hexagon borders?
# Ref: https://stackoverflow.com/questions/51381900/r-overlay-points-and-polygons-with-a-certain-degree-of-tolerance
if(FALSE){
# Read the positives
TSp <- read.csv("../../Data/Period_1/TimeSeries_day_50.csv") %>%
unfold(., "HOST") %>%
mutate(Positive = 1) %>%
st_as_sf(., coords = c("X", "Y"), crs = st_crs(BHx)) %>%
st_join(BHx) %>% data.frame() %>%
group_by(idhex, DATE.CONF) %>%
summarise(cases = sum(Positive), wb_cases = sum(wild.boar), ph_cases = sum(pig.herd))
}
# Read the positives
TSp_sf <- read.csv(here("Data/Period_1/TimeSeries_day_50.csv")) %>%
unfold(., "HOST") %>%
mutate(Positive = 1) %>%
st_as_sf(., coords = c("X", "Y"), crs = st_crs(BHx))
TSp = st_join(TSp_sf, BHx, join = st_nn) %>%
data.frame() %>%
group_by(idhex, DATE.CONF) %>%
summarise(cases = sum(Positive), wb_cases = sum(wild.boar), ph_cases = sum(pig.herd))##
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|======================================================================| 100%# How does the cumulative number of cases per grid looks like?
TSp %>%
dplyr::filter(!is.na(DATE.CONF)) %>%
group_by(idhex) %>%
mutate(cases_cum = cumsum(cases)) %>%
ggplot() +
geom_line(aes(x=DATE.CONF, y=cases_cum, col = idhex))
ggplot() +
geom_sf(data = BHx, fill = "grey") +
geom_sf(data = TSp_sf, col = "yellow", size = 0.5)
# Add the number of cases to the data
TSp_s <- TSp %>%
group_by(idhex) %>%
summarise_at(.vars = c('cases', 'wb_cases', 'ph_cases'), .funs = sum)
# Join to the Hx Shapefile
Hx <- Hx %>%
left_join(TSp_s, by = 'idhex')
# Hx %>%
# data.frame() %>%
# arrange(desc(wb_cases))# Now read the negatives
TSn_sf <- read.csv(here("Data/Period_1/TimeSeries_hunt_negative_test_day_50.csv")) %>%
unfold(., "HOST") %>%
mutate(Negative = 1) %>%
st_as_sf(., coords = c("X", "Y"), crs = st_crs(BHx))
TSn = st_join(TSn_sf, BHx, join = st_nn) %>%
data.frame() %>%
group_by(idhex, DATE.CONF) %>%
summarise(negatives = sum(Negative), wb_cases = sum(wild.boar), ph_cases = 0)##
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|======================================================================| 100%# The above is hunting data only, with no pig herd cases. Hence setting it to 0.
ggplot() +
geom_sf(data = BHx, fill = "grey") +
geom_sf(data = TSn_sf, col = "blue", size = 0.5) +
geom_sf(data = TSp_sf, col = "orange", size = 0.5) +
theme_void()
if(FALSE){
# Now read the negatives
TSn <- read.csv(here("Data/Period_1/TimeSeries_hunt_negative_test_day_50.csv")) %>%
unfold(., "HOST") %>%
mutate(Test = 0) %>%
st_as_sf(., coords = c("X", "Y"), crs = st_crs(BHx)) %>%
st_join(BHx) %>% data.frame() %>%
group_by(idhex) %>%
summarise(tests = sum(Test), wb_tests = sum(wild.boar))
}
HxDF <- Hx %>%
filter(!is.na(Pop)) %>%
data.frame()
HxDF %>%
arrange(desc(cases))## idhex Farms Pop is_outdoor is_commercial multisite B BF F density
## 1 589 1 12 1 0 0 1 0 0 12.000000
## 2 625 1 3 1 0 0 0 0 1 3.000000
## 3 587 3 4408 0 3 41 2 1 0 1469.333333
## 4 556 1 221 0 1 122 0 0 1 221.000000
## 5 591 3 1141 1 2 0 0 1 2 380.333333
## 6 627 2 11 1 0 0 0 0 2 5.500000
## 7 663 2 5105 1 1 0 1 0 1 2552.500000
## 8 517 5 1363 0 3 0 0 1 4 272.600000
## 9 553 2 1334 0 2 75 0 1 1 667.000000
## 10 665 2 2752 0 1 98 0 0 2 1376.000000
## 11 515 2 263 1 1 0 1 0 1 131.500000
## 12 660 4 204 1 1 0 0 1 3 51.000000
## 13 623 2 1445 1 2 17 0 0 2 722.500000
## 14 481 4 6068 1 4 0 1 2 1 1517.000000
## 15 558 1 36 0 0 0 1 0 0 36.000000
## 16 2 1 126 1 1 0 0 0 1 126.000000
## 17 4 3 9139 2 3 55 1 1 1 3046.333333
## 18 6 2 14466 0 2 42 1 0 1 7233.000000
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## 255 NA NA NA 3.493824e-06 52.407357 0.294656489
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## 296 0.67272727 0.000000000 44.0 1.074020e-06 16.110296 0.834862385
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## 323 NA NA NA 2.209826e-06 33.147393 0.269525268
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## 336 0.49090909 0.009090909 22.0 1.074020e-06 16.110296 0.854517611
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## 345 1.03636364 0.000000000 81.0 1.079738e-06 16.196070 0.827480916
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## 347 NA NA NA 4.591149e-06 68.867241 0.857798165
## 348 0.40000000 0.009090909 9.0 2.181853e-06 32.727800 0.848623853
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## 357 0.20000000 0.000000000 62.0 1.737530e-06 26.062952 0.596946565
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## 695 NA NA NA 1.109189e-06 16.637836 0.656488550
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## 705 NA NA NA 1.261802e-06 18.927033 0.288990826
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## 708 0.15454545 0.000000000 2.0 8.166039e-07 12.249058 0.497709924
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## 712 NA NA NA 5.413306e-07 8.119959 0.635114504
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## 714 0.10000000 0.000000000 4.0 5.413306e-07 8.119959 0.779816514
## 715 0.05454545 0.000000000 2.0 5.416999e-07 8.125499 0.679938744
## 716 0.05454545 0.000000000 2.0 5.416999e-07 8.125499 0.679938744
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## 718 NA NA NA 5.442762e-07 8.164143 0.512232416
## 719 NA NA NA 5.443453e-07 8.165180 0.636641221
## 720 1.03636364 0.000000000 38.0 5.443453e-07 8.165180 0.700763359
## 721 NA NA NA 5.443453e-07 8.165180 0.582568807
## 722 NA NA NA 5.443453e-07 8.165180 0.614678899
## 723 0.03636364 0.000000000 1.0 1.242093e-06 18.631402 0.473282443
## 724 0.03636364 0.000000000 1.0 1.242093e-06 18.631402 0.473282443
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## 726 NA NA NA 4.823867e-06 72.358006 0.221374046
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## 731 NA NA NA 8.208682e-07 12.313023 0.639143731
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## 733 NA NA NA 8.336613e-07 12.504919 0.266055046
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## 736 0.10000000 0.000000000 243.0 8.166099e-07 12.249148 0.484709480
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## 738 NA NA NA 8.183638e-07 12.275458 0.515290520
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## 740 0.05454545 0.000000000 3.0 5.413306e-07 8.119959 0.587155963
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## 757 NA NA NA 5.496185e-07 8.244278 0.490825688
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## 760 1.19090909 0.000000000 9.5 2.789446e-06 41.841693 0.359877489
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## 787 0.05454545 0.000000000 12.0 1.630525e-07 2.445787 0.692189893
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## 789 NA NA NA 3.145284e-07 4.717926 0.641653905
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## 795 NA NA NA 2.864190e-07 4.296284 0.464831804
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## 797 0.11818182 0.000000000 47.5 2.720020e-06 40.800293 0.513761468
## 798 0.11818182 0.000000000 47.5 2.720020e-06 40.800293 0.513761468
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## 802 NA NA NA 3.419118e-06 51.286766 0.416793893
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## 808 0.27272727 0.000000000 64.0 8.872840e-07 13.309260 0.775572519
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## 810 0.30909091 0.000000000 48.0 8.200101e-07 12.300152 0.760305344
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## 812 0.13636364 0.000000000 145.0 8.166039e-07 12.249058 0.607033639
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## 815 NA NA NA 8.168020e-07 12.252030 0.359327217
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## 817 NA NA NA 8.166939e-07 12.250409 0.276758410
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## 819 NA NA NA 8.204285e-07 12.306428 0.294027565
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## 823 NA NA NA 8.205308e-07 12.307962 0.730474732
## 824 NA NA NA 2.330039e-07 3.495059 0.357798165
## 825 NA NA NA 2.330039e-07 3.495059 0.357798165
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## 828 0.06363636 0.000000000 0.0 1.565664e-07 2.348496 0.707951070
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## 831 NA NA NA 1.565664e-07 2.348496 0.396946565
## 832 NA NA NA 1.565664e-07 2.348496 0.429007634
## 833 NA NA NA 3.442827e-07 5.164241 0.284403670
## 834 NA NA NA 3.442827e-07 5.164241 0.284403670
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## 836 NA NA NA 3.969555e-06 59.543319 0.555045872
## 837 0.04545455 0.000000000 5.5 3.993019e-06 59.895287 0.580152672
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## 842 NA NA NA 1.923953e-06 28.859298 0.558103976
## 843 NA NA NA 1.923953e-06 28.859298 0.558103976
## 844 NA NA NA 8.166039e-07 12.249058 0.638167939
## 845 0.70000000 0.000000000 57.0 8.166039e-07 12.249058 0.668702290
## 846 0.05454545 0.000000000 5.0 8.166039e-07 12.249058 0.732415902
## 847 0.13636364 0.000000000 10.0 8.166039e-07 12.249058 0.689602446
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## 857 NA NA NA 8.205308e-07 12.307962 0.605504587
## 858 0.52727273 0.000000000 50.0 8.205308e-07 12.307962 0.775229358
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## 869 NA NA NA 3.752508e-06 56.287614 0.533639144
## 870 0.04545455 0.000000000 8.0 3.993019e-06 59.895287 0.900763359
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## 876 0.21818182 0.000000000 147.0 4.171918e-06 62.578770 0.604900459
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## 881 NA NA NA 8.166039e-07 12.249058 0.641221374
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## 887 0.05454545 0.000000000 12.0 8.342393e-07 12.513589 0.636641221
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## 889 NA NA NA 8.205308e-07 12.307962 0.376146789
## 890 NA NA NA 8.205308e-07 12.307962 0.366972477
## 891 0.08181818 0.000000000 6.0 8.205308e-07 12.307962 0.293577982
## 892 0.08181818 0.000000000 376.5 8.205308e-07 12.307962 0.528244275
## 893 NA NA NA 8.205308e-07 12.307962 0.553516820
## 894 NA NA NA 8.205308e-07 12.307962 0.133027523
## 895 NA NA NA 1.565664e-07 2.348496 0.466360856
## 896 NA NA NA 1.565664e-07 2.348496 0.659541985
## 897 0.97272727 0.000000000 30.0 1.565664e-07 2.348496 0.527522936
## 898 0.49090909 0.000000000 51.0 1.565664e-07 2.348496 0.450229709
## 899 NA NA NA 1.565664e-07 2.348496 0.798165138
## 900 NA NA NA 1.565664e-07 2.348496 0.672782875
## 901 0.09090909 0.000000000 29.0 3.993019e-06 59.895287 0.383792049
## 902 0.06363636 0.000000000 1.5 3.993019e-06 59.895287 0.746177370
## 903 0.07272727 0.000000000 28.0 3.997414e-06 59.961207 0.819847328
## 904 0.07272727 0.000000000 28.0 3.997414e-06 59.961207 0.819847328
## 905 0.46363636 0.000000000 29.0 4.335800e-06 65.036993 0.796946565
## 906 0.46363636 0.000000000 29.0 4.335800e-06 65.036993 0.796946565
## 907 0.12727273 0.000000000 2.0 4.352829e-06 65.292430 0.563988095
## 908 NA NA NA 4.352829e-06 65.292430 0.204580153
## 909 0.04545455 0.000000000 4.0 3.683375e-06 55.250631 0.648854962
## 910 0.04545455 0.000000000 4.0 3.683375e-06 55.250631 0.648854962
## 911 NA NA NA 8.656642e-07 12.984963 0.629969419
## 912 NA NA NA 8.656642e-07 12.984963 0.629969419
## 913 NA NA NA 8.656642e-07 12.984963 0.629969419
## 914 0.54545455 0.000000000 54.0 8.331532e-07 12.497298 0.605504587
## 915 0.54545455 0.000000000 54.0 8.331532e-07 12.497298 0.605504587
## 916 0.11818182 0.000000000 1.0 1.114666e-06 16.719993 0.431192661
## 917 0.11818182 0.000000000 1.0 1.114666e-06 16.719993 0.431192661
## 918 0.11818182 0.000000000 1.0 1.114666e-06 16.719993 0.431192661
## 919 0.87272727 0.000000000 33.0 8.584776e-07 12.877164 0.482442748
## 920 0.87272727 0.000000000 33.0 8.584776e-07 12.877164 0.482442748
## 921 NA NA NA 8.221013e-07 12.331520 0.417431193
## 922 NA NA NA 8.221013e-07 12.331520 0.417431193
## 923 NA NA NA 8.274380e-07 12.411571 0.326186830
## 924 NA NA NA 8.274380e-07 12.411571 0.326186830
## 925 0.06363636 0.000000000 2.0 8.225793e-07 12.338690 0.244648318
## 926 0.06363636 0.000000000 2.0 8.225793e-07 12.338690 0.244648318
## 927 0.27272727 0.000000000 93.0 8.205308e-07 12.307962 0.487022901
## 928 0.04545455 0.000000000 4.5 8.206048e-07 12.309073 0.648854962
## 929 0.04545455 0.000000000 4.5 8.206048e-07 12.309073 0.648854962
## 930 NA NA NA 1.565664e-07 2.348496 0.285932722
## 931 0.80909091 0.000000000 36.0 1.565664e-07 2.348496 0.649847095
## 932 NA NA NA 1.565664e-07 2.348496 0.577099237
## 933 NA NA NA 1.565664e-07 2.348496 0.393893130
## 934 0.05454545 0.000000000 1.0 1.565664e-07 2.348496 0.836641221
## 935 0.29090909 0.000000000 79.0 1.565664e-07 2.348496 0.743511450
## 936 NA NA NA 1.565664e-07 2.348496 0.395419847
## 937 NA NA NA 3.470933e-06 52.063996 0.472477064
## 938 NA NA NA 3.470933e-06 52.063996 0.472477064
## 939 NA NA NA 3.993019e-06 59.895287 0.770642202
## 940 NA NA NA 3.993019e-06 59.895287 0.800000000
## 941 NA NA NA 4.190529e-06 62.857939 0.629969419
## 942 NA NA NA 4.190529e-06 62.857939 0.629969419
## 943 0.06363636 0.000000000 4.0 4.352829e-06 65.292430 0.755351682
## 944 0.07272727 0.000000000 0.0 4.347948e-06 65.219220 0.467075038
## 945 0.07272727 0.000000000 0.0 4.347948e-06 65.219220 0.467075038
## 946 NA NA NA 2.539990e-06 38.099853 0.614678899
## 947 NA NA NA 2.539990e-06 38.099853 0.614678899
## 948 0.04545455 0.000000000 1.0 1.256279e-06 18.844192 0.705343511
## 949 0.04545455 0.000000000 1.0 1.256279e-06 18.844192 0.705343511
## 950 0.04545455 0.000000000 1.0 1.256279e-06 18.844192 0.705343511
## 951 NA NA NA 9.562590e-07 14.343885 0.636641221
## 952 NA NA NA 9.562590e-07 14.343885 0.636641221
## 953 NA NA NA 1.163606e-06 17.454094 0.301223242
## 954 NA NA NA 1.163606e-06 17.454094 0.301223242
## 955 NA NA NA 1.128792e-06 16.931887 0.454128440
## 956 NA NA NA 1.128792e-06 16.931887 0.454128440
## 957 0.39090909 0.000000000 25.0 8.310987e-07 12.466480 0.607633588
## 958 0.39090909 0.000000000 25.0 8.310987e-07 12.466480 0.607633588
## 959 NA NA NA 8.419393e-07 12.629089 0.442748092
## 960 NA NA NA 8.419393e-07 12.629089 0.442748092
## 961 NA NA NA 8.423484e-07 12.635226 0.383206107
## 962 NA NA NA 8.423484e-07 12.635226 0.383206107
## 963 0.10909091 0.000000000 9.0 8.241102e-07 12.361653 0.421374046
## 964 0.10909091 0.000000000 9.0 8.241102e-07 12.361653 0.421374046
## 965 NA NA NA 8.360758e-07 12.541137 0.557251908
## 966 NA NA NA 8.360758e-07 12.541137 0.557251908
## 967 0.08181818 0.000000000 5.0 1.565664e-07 2.348496 0.664122137
## 968 0.27272727 0.000000000 115.0 1.565664e-07 2.348496 0.643730887
## 969 NA NA NA 1.565664e-07 2.348496 0.821100917
## 970 NA NA NA 1.565664e-07 2.348496 0.879204893
## 971 NA NA NA 2.263019e-07 3.394529 0.477794793
## 972 NA NA NA 2.263019e-07 3.394529 0.477794793
## 973 NA NA NA 1.378289e-06 20.674329 0.324159021
## 974 NA NA NA 1.378289e-06 20.674329 0.324159021
## 975 NA NA NA 1.378289e-06 20.674329 0.324159021
## 976 NA NA NA 3.874721e-06 58.120820 0.508422665
## 977 NA NA NA 3.874721e-06 58.120820 0.508422665
## 978 0.20909091 0.000000000 70.5 3.993019e-06 59.895287 0.707503828
## 979 NA NA NA 4.111486e-06 61.672295 0.800918836
## 980 NA NA NA 4.111486e-06 61.672295 0.800918836
## 981 NA NA NA 4.352829e-06 65.292430 0.498470948
## 982 0.10909091 0.000000000 4.0 4.163121e-06 62.446817 0.741071429
## 983 0.10909091 0.000000000 4.0 4.163121e-06 62.446817 0.741071429
## 984 0.20909091 0.000000000 9.0 1.492248e-06 22.383713 0.804281346
## 985 0.20909091 0.000000000 9.0 1.492248e-06 22.383713 0.804281346
## 986 NA NA NA 1.165742e-06 17.486125 0.831804281
## 987 0.06363636 0.000000000 4.0 1.165742e-06 17.486125 0.663093415
## 988 NA NA NA 1.165742e-06 17.486125 0.238897397
## 989 0.09090909 0.000000000 153.0 1.165742e-06 17.486125 0.512232416
## 990 NA NA NA 9.920183e-07 14.880274 0.396024465
## 991 NA NA NA 9.920183e-07 14.880274 0.396024465
## 992 NA NA NA 9.920183e-07 14.880274 0.396024465
## 993 NA NA NA 8.428597e-07 12.642896 0.576452599
## 994 NA NA NA 8.428597e-07 12.642896 0.340978593
## 995 0.10909091 0.000000000 1.0 8.422110e-07 12.633165 0.434250765
## 996 0.10909091 0.000000000 1.0 8.422110e-07 12.633165 0.434250765
## 997 NA NA NA 8.409136e-07 12.613704 0.486238532
## 998 NA NA NA 8.409136e-07 12.613704 0.486238532
## 999 NA NA NA 8.428597e-07 12.642896 0.241221374
## 1000 NA NA NA 2.543241e-07 3.814861 0.701378254
## 1001 NA NA NA 2.543241e-07 3.814861 0.701378254
## 1002 0.08181818 0.000000000 2.0 1.565664e-07 2.348496 0.655963303
## 1003 NA NA NA 3.757262e-07 5.635893 0.631498471
## 1004 NA NA NA 3.757262e-07 5.635893 0.631498471
## 1005 0.03636364 0.000000000 12.0 6.542419e-07 9.813628 0.821100917
## 1006 0.03636364 0.000000000 12.0 6.542419e-07 9.813628 0.821100917
## 1007 0.27272727 0.000000000 47.0 7.318610e-07 10.977915 0.545871560
## 1008 0.27272727 0.000000000 47.0 7.318610e-07 10.977915 0.545871560
## 1009 0.59090909 0.000000000 19.0 8.973723e-07 13.460585 0.677370031
## 1010 0.59090909 0.000000000 19.0 8.973723e-07 13.460585 0.677370031
## 1011 NA NA NA 2.216540e-06 33.248102 0.264525994
## 1012 NA NA NA 2.216540e-06 33.248102 0.264525994
## 1013 0.15454545 0.000000000 72.0 3.993019e-06 59.895287 0.781345566
## 1014 0.05454545 0.000000000 3.0 3.999062e-06 59.985926 0.865648855
## 1015 0.05454545 0.000000000 3.0 3.999062e-06 59.985926 0.865648855
## 1016 NA NA NA 4.307715e-06 64.615724 0.467889908
## 1017 NA NA NA 4.307715e-06 64.615724 0.467889908
## 1018 NA NA NA 4.352829e-06 65.292430 0.526717557
## 1019 0.06363636 0.000000000 0.5 2.625476e-06 39.382143 0.847328244
## 1020 0.06363636 0.000000000 0.5 2.625476e-06 39.382143 0.847328244
## 1021 0.07272727 0.000000000 2.0 1.165742e-06 17.486125 0.882442748
## 1022 0.05454545 0.000000000 1.0 1.165742e-06 17.486125 0.792048930
## 1023 NA NA NA 1.165742e-06 17.486125 0.570992366
## 1024 0.89090909 0.000000000 96.0 1.165742e-06 17.486125 0.376146789
## 1025 NA NA NA 1.141550e-06 17.123253 0.382262997
## 1026 NA NA NA 1.141550e-06 17.123253 0.382262997
## 1027 NA NA NA 8.547086e-07 12.820629 0.581039755
## 1028 NA NA NA 8.547086e-07 12.820629 0.581039755
## 1029 NA NA NA 8.428597e-07 12.642896 0.622324159
## 1030 NA NA NA 8.428597e-07 12.642896 0.532110092
## 1031 0.06363636 0.000000000 1.0 8.428597e-07 12.642896 0.417431193
## 1032 NA NA NA 8.428597e-07 12.642896 0.392036753
## 1033 0.10000000 0.000000000 6.0 8.428597e-07 12.642896 0.425727412
## 1034 NA NA NA 9.009345e-07 13.514017 0.421374046
## 1035 NA NA NA 9.009345e-07 13.514017 0.421374046
## 1036 NA NA NA 6.291298e-07 9.436947 0.862385321
## 1037 NA NA NA 6.291298e-07 9.436947 0.862385321
## 1038 NA NA NA 5.229743e-07 7.844614 0.776758410
## 1039 NA NA NA 5.229743e-07 7.844614 0.776758410
## 1040 NA NA NA 9.030796e-07 13.546194 0.762996942
## 1041 0.07272727 0.000000000 12.0 9.030796e-07 13.546194 0.782874618
## 1042 NA NA NA 9.030796e-07 13.546194 0.877488515
## 1043 NA NA NA 9.881238e-07 14.821857 0.723241590
## 1044 NA NA NA 9.881238e-07 14.821857 0.723241590
## 1045 0.20909091 0.000000000 40.5 3.937951e-06 59.069264 0.647328244
## 1046 0.20909091 0.000000000 40.5 3.937951e-06 59.069264 0.647328244
## 1047 NA NA NA 3.993019e-06 59.895287 0.253822630
## 1048 NA NA NA 3.993019e-06 59.895287 0.241590214
## 1049 0.45454545 0.000000000 46.0 4.309451e-06 64.641770 0.438837920
## 1050 0.45454545 0.000000000 46.0 4.309451e-06 64.641770 0.438837920
## 1051 0.44545455 0.000000000 24.0 4.352829e-06 65.292430 0.607142857
## 1052 0.09090909 0.000000000 65.0 1.268056e-06 19.020838 0.799694190
## 1053 0.09090909 0.000000000 65.0 1.268056e-06 19.020838 0.799694190
## 1054 NA NA NA 1.165742e-06 17.486125 0.814984709
## 1055 NA NA NA 1.165742e-06 17.486125 0.545871560
## 1056 0.14545455 0.000000000 3.0 1.165742e-06 17.486125 0.388379205
## 1057 NA NA NA 1.165742e-06 17.486125 0.215596330
## 1058 NA NA NA 1.160797e-06 17.411956 0.286370597
## 1059 NA NA NA 1.160797e-06 17.411956 0.286370597
## 1060 0.07272727 0.000000000 11.0 8.428597e-07 12.642896 0.376146789
## 1061 0.05454545 0.000000000 1.0 8.428597e-07 12.642896 0.437308869
## 1062 0.38181818 0.000000000 68.0 8.428597e-07 12.642896 0.379204893
## 1063 1.67272727 0.000000000 32.0 8.428597e-07 12.642896 0.331297710
## 1064 NA NA NA 9.030796e-07 13.546194 0.619266055
## 1065 NA NA NA 9.030796e-07 13.546194 0.695718654
## 1066 0.37272727 0.000000000 106.0 9.030796e-07 13.546194 0.900611621
## 1067 0.37272727 0.000000000 106.0 9.030796e-07 13.546194 0.900611621
## 1068 0.16363636 0.000000000 46.0 9.030796e-07 13.546194 0.856488550
## 1069 NA NA NA 9.030796e-07 13.546194 0.893129771
## 1070 0.06363636 0.000000000 2.0 9.030796e-07 13.546194 0.862595420
## 1071 0.29090909 0.000000000 41.0 9.030796e-07 13.546194 0.914503817
## 1072 NA NA NA 9.456017e-07 14.184026 0.867175573
## 1073 NA NA NA 9.456017e-07 14.184026 0.867175573
## 1074 NA NA NA 9.030796e-07 13.546194 0.262595420
## 1075 NA NA NA 4.352829e-06 65.292430 0.085758040
## 1076 NA NA NA 4.352829e-06 65.292430 0.157492355
## 1077 NA NA NA 1.870849e-06 28.062741 0.123664122
## 1078 NA NA NA 1.870849e-06 28.062741 0.123664122
## 1079 NA NA NA 1.165742e-06 17.486125 0.849923430
## 1080 0.13636364 0.000000000 14.5 1.165742e-06 17.486125 0.768759571
## 1081 NA NA NA 1.165742e-06 17.486125 0.562691131
## 1082 0.10909091 0.000000000 4.0 1.165742e-06 17.486125 0.158778626
## 1083 NA NA NA 1.165742e-06 17.486125 0.267175573
## 1084 NA NA NA 1.165742e-06 17.486125 0.216793893
## 1085 NA NA NA 8.428597e-07 12.642896 0.361832061
## 1086 0.09090909 0.000000000 3.0 8.428597e-07 12.642896 0.366412214
## 1087 NA NA NA 8.428597e-07 12.642896 0.371559633
## 1088 NA NA NA 8.428597e-07 12.642896 0.039816233
## 1089 0.59090909 0.000000000 61.0 9.030796e-07 13.546194 0.645801527
## 1090 NA NA NA 9.030796e-07 13.546194 0.845801527
## 1091 0.09090909 0.000000000 6.0 9.030796e-07 13.546194 0.865237366
## 1092 NA NA NA 9.030796e-07 13.546194 0.862174579
## 1093 NA NA NA 9.030796e-07 13.546194 0.906870229
## 1094 0.20909091 0.000000000 5.5 9.030796e-07 13.546194 0.857798165
## 1095 NA NA NA 9.030796e-07 13.546194 0.724349158
## 1096 NA NA NA 9.030796e-07 13.546194 0.877488515
## 1097 NA NA NA 9.030796e-07 13.546194 0.801526718
## 1098 0.05454545 0.000000000 2.0 1.165742e-06 17.486125 0.364885496
## 1099 0.05454545 0.000000000 9.0 1.165742e-06 17.486125 0.858015267
## 1100 NA NA NA 1.165742e-06 17.486125 0.564885496
## 1101 NA NA NA 8.428597e-07 12.642896 0.306278714
## 1102 0.08181818 0.000000000 2.0 8.428597e-07 12.642896 0.114678899
## 1103 0.07272727 0.000000000 1.0 9.030796e-07 13.546194 0.512232416
## 1104 0.18181818 0.000000000 49.0 9.030796e-07 13.546194 0.860856269
## 1105 0.05454545 0.000000000 9.0 9.030796e-07 13.546194 0.845801527
## 1106 0.36363636 0.000000000 89.0 9.030796e-07 13.546194 0.781679389
## 1107 NA NA NA 9.030796e-07 13.546194 0.677862595
## 1108 0.09090909 0.000000000 10.0 9.030796e-07 13.546194 0.818320611
## 1109 NA NA NA 9.030796e-07 13.546194 0.311450382
## 1110 0.85454545 0.000000000 80.0 9.030796e-07 13.546194 0.475535168
## 1111 NA NA NA 9.030796e-07 13.546194 0.743119266
## 1112 0.60909091 0.000000000 20.5 9.030796e-07 13.546194 0.676335878
## 1113 NA NA NA 9.030796e-07 13.546194 0.539755352
## 1114 NA NA NA 9.030796e-07 13.546194 0.539050536
## 1115 NA NA NA 9.030796e-07 13.546194 0.323124043
## 1116 NA NA NA 9.030796e-07 13.546194 0.220843672
## 1117 NA NA NA 9.030796e-07 13.546194 0.476426799
## Forest Urban cases wb_cases ph_cases
## 1 0.894495413 0.000000000 111 111 0
## 2 0.802752294 0.004587156 91 90 1
## 3 0.834862385 0.003058104 46 46 0
## 4 0.668702290 0.007633588 42 42 0
## 5 0.763803681 0.010736196 34 33 1
## 6 0.885496183 0.012213740 34 34 0
## 7 0.670229008 0.003053435 18 18 0
## 8 0.923664122 0.010687023 12 12 0
## 9 0.908256881 0.004587156 10 10 0
## 10 0.767228178 0.001531394 4 4 0
## 11 0.932824427 0.003053435 3 3 0
## 12 0.343511450 0.010687023 3 3 0
## 13 0.630534351 0.007633588 2 1 1
## 14 0.516819572 0.111620795 1 1 0
## 15 0.462595420 0.029007634 1 1 0
## 16 0.149758454 0.000000000 NA NA NA
## 17 0.389578164 0.004962779 NA NA NA
## 18 0.189312977 0.004580153 NA NA NA
## 19 0.319083969 0.044274809 NA NA NA
## 20 0.497709924 0.004580153 NA NA NA
## 21 0.648854962 0.000000000 NA NA NA
## 22 0.617737003 0.039755352 NA NA NA
## 23 0.400611621 0.022935780 NA NA NA
## 24 0.245801527 0.035114504 NA NA NA
## 25 0.250381679 0.001526718 NA NA NA
## 26 0.250381679 0.001526718 NA NA NA
## 27 0.399693721 0.001531394 NA NA NA
## 28 0.399693721 0.001531394 NA NA NA
## 29 0.670149254 0.016417910 NA NA NA
## 30 0.654961832 0.007633588 NA NA NA
## 31 0.670229008 0.018320611 NA NA NA
## 32 0.847094801 0.009174312 NA NA NA
## 33 0.113149847 0.081039755 NA NA NA
## 34 0.088549618 0.085496183 NA NA NA
## 35 0.044274809 0.000000000 NA NA NA
## 36 0.000000000 0.000000000 NA NA NA
## 37 0.091603053 0.000000000 NA NA NA
## 38 0.056661562 0.019908116 NA NA NA
## 39 0.056661562 0.019908116 NA NA NA
## 40 0.105666156 0.000000000 NA NA NA
## 41 0.489296636 0.000000000 NA NA NA
## 42 0.519083969 0.000000000 NA NA NA
## 43 0.749618321 0.009160305 NA NA NA
## 44 0.653435115 0.000000000 NA NA NA
## 45 0.653435115 0.000000000 NA NA NA
## 46 0.653435115 0.000000000 NA NA NA
## 47 0.780152672 0.000000000 NA NA NA
## 48 0.780152672 0.000000000 NA NA NA
## 49 0.780152672 0.000000000 NA NA NA
## 50 0.609160305 0.003053435 NA NA NA
## 51 0.609160305 0.003053435 NA NA NA
## 52 0.609160305 0.016793893 NA NA NA
## 53 0.171516080 0.059724349 NA NA NA
## 54 0.001531394 0.251148545 NA NA NA
## 55 0.001526718 0.016793893 NA NA NA
## 56 0.014056225 0.004016064 NA NA NA
## 57 0.173047473 0.015313936 NA NA NA
## 58 0.319083969 0.001526718 NA NA NA
## 59 0.528244275 0.007633588 NA NA NA
## 60 0.259541985 0.001526718 NA NA NA
## 61 0.259541985 0.001526718 NA NA NA
## 62 0.476335878 0.016793893 NA NA NA
## 63 0.138931298 0.000000000 NA NA NA
## 64 0.138931298 0.000000000 NA NA NA
## 65 0.329770992 0.000000000 NA NA NA
## 66 0.699236641 0.003053435 NA NA NA
## 67 0.637614679 0.004587156 NA NA NA
## 68 0.686544343 0.022935780 NA NA NA
## 69 0.621374046 0.030534351 NA NA NA
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## 795 POLYGON ((753969.5 6480703,...
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## 799 POLYGON ((783969.5 6480703,...
## 800 POLYGON ((783969.5 6480703,...
## 801 POLYGON ((798969.5 6480703,...
## 802 POLYGON ((798969.5 6480703,...
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## 806 POLYGON ((828969.5 6480703,...
## 807 POLYGON ((843969.5 6480703,...
## 808 POLYGON ((843969.5 6480703,...
## 809 POLYGON ((858969.5 6480703,...
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## 819 POLYGON ((948969.5 6480703,...
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## 824 POLYGON ((656469.5 6493694,...
## 825 POLYGON ((656469.5 6493694,...
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## 833 POLYGON ((761469.5 6493694,...
## 834 POLYGON ((761469.5 6493694,...
## 835 POLYGON ((776469.5 6493694,...
## 836 POLYGON ((776469.5 6493694,...
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## 839 POLYGON ((821469.5 6493694,...
## 840 POLYGON ((821469.5 6493694,...
## 841 POLYGON ((836469.5 6493694,...
## 842 POLYGON ((836469.5 6493694,...
## 843 POLYGON ((836469.5 6493694,...
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## 845 POLYGON ((866469.5 6493694,...
## 846 POLYGON ((881469.5 6493694,...
## 847 POLYGON ((896469.5 6493694,...
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## 850 POLYGON ((911469.5 6493694,...
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## 852 POLYGON ((926469.5 6493694,...
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## 881 POLYGON ((858969.5 6506684,...
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## 883 POLYGON ((888969.5 6506684,...
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## 885 POLYGON ((903969.5 6506684,...
## 886 POLYGON ((903969.5 6506684,...
## 887 POLYGON ((903969.5 6506684,...
## 888 POLYGON ((918969.5 6506684,...
## 889 POLYGON ((933969.5 6506684,...
## 890 POLYGON ((948969.5 6506684,...
## 891 POLYGON ((963969.5 6506684,...
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## 893 POLYGON ((993969.5 6506684,...
## 894 POLYGON ((1008970 6506684, ...
## 895 POLYGON ((656469.5 6519675,...
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## 900 POLYGON ((731469.5 6519675,...
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## 917 POLYGON ((896469.5 6519675,...
## 918 POLYGON ((896469.5 6519675,...
## 919 POLYGON ((911469.5 6519675,...
## 920 POLYGON ((911469.5 6519675,...
## 921 POLYGON ((926469.5 6519675,...
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## 924 POLYGON ((941469.5 6519675,...
## 925 POLYGON ((956469.5 6519675,...
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## 940 POLYGON ((783969.5 6532665,...
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## 942 POLYGON ((798969.5 6532665,...
## 943 POLYGON ((813969.5 6532665,...
## 944 POLYGON ((828969.5 6532665,...
## 945 POLYGON ((828969.5 6532665,...
## 946 POLYGON ((843969.5 6532665,...
## 947 POLYGON ((843969.5 6532665,...
## 948 POLYGON ((858969.5 6532665,...
## 949 POLYGON ((858969.5 6532665,...
## 950 POLYGON ((858969.5 6532665,...
## 951 POLYGON ((873969.5 6532665,...
## 952 POLYGON ((873969.5 6532665,...
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## 972 POLYGON ((731469.5 6545655,...
## 973 POLYGON ((746469.5 6545655,...
## 974 POLYGON ((746469.5 6545655,...
## 975 POLYGON ((746469.5 6545655,...
## 976 POLYGON ((761469.5 6545655,...
## 977 POLYGON ((761469.5 6545655,...
## 978 POLYGON ((776469.5 6545655,...
## 979 POLYGON ((791469.5 6545655,...
## 980 POLYGON ((791469.5 6545655,...
## 981 POLYGON ((806469.5 6545655,...
## 982 POLYGON ((821469.5 6545655,...
## 983 POLYGON ((821469.5 6545655,...
## 984 POLYGON ((836469.5 6545655,...
## 985 POLYGON ((836469.5 6545655,...
## 986 POLYGON ((851469.5 6545655,...
## 987 POLYGON ((866469.5 6545655,...
## 988 POLYGON ((881469.5 6545655,...
## 989 POLYGON ((896469.5 6545655,...
## 990 POLYGON ((911469.5 6545655,...
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## 992 POLYGON ((911469.5 6545655,...
## 993 POLYGON ((926469.5 6545655,...
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## 999 POLYGON ((1001470 6545655, ...
## 1000 POLYGON ((663969.5 6558646,...
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## 1006 POLYGON ((708969.5 6558646,...
## 1007 POLYGON ((723969.5 6558646,...
## 1008 POLYGON ((723969.5 6558646,...
## 1009 POLYGON ((738969.5 6558646,...
## 1010 POLYGON ((738969.5 6558646,...
## 1011 POLYGON ((753969.5 6558646,...
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## 1014 POLYGON ((783969.5 6558646,...
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## 1042 POLYGON ((731469.5 6571636,...
## 1043 POLYGON ((746469.5 6571636,...
## 1044 POLYGON ((746469.5 6571636,...
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## 1046 POLYGON ((761469.5 6571636,...
## 1047 POLYGON ((776469.5 6571636,...
## 1048 POLYGON ((791469.5 6571636,...
## 1049 POLYGON ((806469.5 6571636,...
## 1050 POLYGON ((806469.5 6571636,...
## 1051 POLYGON ((821469.5 6571636,...
## 1052 POLYGON ((836469.5 6571636,...
## 1053 POLYGON ((836469.5 6571636,...
## 1054 POLYGON ((851469.5 6571636,...
## 1055 POLYGON ((866469.5 6571636,...
## 1056 POLYGON ((881469.5 6571636,...
## 1057 POLYGON ((896469.5 6571636,...
## 1058 POLYGON ((911469.5 6571636,...
## 1059 POLYGON ((911469.5 6571636,...
## 1060 POLYGON ((941469.5 6571636,...
## 1061 POLYGON ((956469.5 6571636,...
## 1062 POLYGON ((971469.5 6571636,...
## 1063 POLYGON ((986469.5 6571636,...
## 1064 POLYGON ((648969.5 6584626,...
## 1065 POLYGON ((663969.5 6584626,...
## 1066 POLYGON ((678969.5 6584626,...
## 1067 POLYGON ((678969.5 6584626,...
## 1068 POLYGON ((693969.5 6584626,...
## 1069 POLYGON ((708969.5 6584626,...
## 1070 POLYGON ((723969.5 6584626,...
## 1071 POLYGON ((738969.5 6584626,...
## 1072 POLYGON ((753969.5 6584626,...
## 1073 POLYGON ((753969.5 6584626,...
## 1074 POLYGON ((768969.5 6584626,...
## 1075 POLYGON ((798969.5 6584626,...
## 1076 POLYGON ((813969.5 6584626,...
## 1077 POLYGON ((828969.5 6584626,...
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## 1079 POLYGON ((843969.5 6584626,...
## 1080 POLYGON ((858969.5 6584626,...
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## 1082 POLYGON ((888969.5 6584626,...
## 1083 POLYGON ((918969.5 6584626,...
## 1084 POLYGON ((933969.5 6584626,...
## 1085 POLYGON ((948969.5 6584626,...
## 1086 POLYGON ((963969.5 6584626,...
## 1087 POLYGON ((978969.5 6584626,...
## 1088 POLYGON ((993969.5 6584626,...
## 1089 POLYGON ((641469.5 6597617,...
## 1090 POLYGON ((656469.5 6597617,...
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## 1092 POLYGON ((686469.5 6597617,...
## 1093 POLYGON ((701469.5 6597617,...
## 1094 POLYGON ((716469.5 6597617,...
## 1095 POLYGON ((731469.5 6597617,...
## 1096 POLYGON ((746469.5 6597617,...
## 1097 POLYGON ((761469.5 6597617,...
## 1098 POLYGON ((836469.5 6597617,...
## 1099 POLYGON ((851469.5 6597617,...
## 1100 POLYGON ((866469.5 6597617,...
## 1101 POLYGON ((971469.5 6597617,...
## 1102 POLYGON ((986469.5 6597617,...
## 1103 POLYGON ((663969.5 6610607,...
## 1104 POLYGON ((678969.5 6610607,...
## 1105 POLYGON ((693969.5 6610607,...
## 1106 POLYGON ((708969.5 6610607,...
## 1107 POLYGON ((723969.5 6610607,...
## 1108 POLYGON ((738969.5 6610607,...
## 1109 POLYGON ((753969.5 6610607,...
## 1110 POLYGON ((671469.5 6623598,...
## 1111 POLYGON ((686469.5 6623598,...
## 1112 POLYGON ((701469.5 6623598,...
## 1113 POLYGON ((716469.5 6623598,...
## 1114 POLYGON ((731469.5 6623598,...
## 1115 POLYGON ((746469.5 6623598,...
## 1116 POLYGON ((678969.5 6636588,...
## 1117 POLYGON ((693969.5 6636588,...HxDF %>%
# mutate(wb_bool = ifelse(Result != 0 & wild.boar != 0, 1, 0)) %>% # For wild boars
mutate(wb_bool = ifelse(cases != 0 & ph_cases != 0, 1, 0)) %>% # For pig herds
group_by(wb_bool) %>%
summarise_at(.vars = c('is_outdoor', 'is_commercial', 'multisite', 'B', 'BF', 'F', 'density', 'Agricultural', 'Forest', 'Urban', 'E_WB'), .funs = mean)## # A tibble: 3 x 12
## wb_bool is_outdoor is_commercial multisite B BF F density
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0.5 1.42 28 0.583 0.5 1.33 693.
## 2 1 1 1.33 5.67 0 0.333 1.67 369.
## 3 NA 1.33 4.61 76.0 0.570 1.79 3.48 886.
## # … with 4 more variables: Agricultural <dbl>, Forest <dbl>, Urban <dbl>,
## # E_WB <dbl># I exchanged Result for cases and also ph_cases for pig.herd above.
# Not sure if the above was what was aimed at though
if(FALSE){
HxDF %>%
# mutate(wb_bool = ifelse(Result != 0 & wild.boar != 0, 1, 0)) %>% # For wild boars
mutate(wb_bool = ifelse(Result != 0 & pig.herd != 0, 1, 0)) %>% # For pig herds
group_by(wb_bool) %>%
summarise_at(.vars = c('is_outdoor', 'is_commercial', 'multisite', 'B', 'BF', 'F', 'density', 'Agricultural', 'Forest', 'Urban', 'E_WB'), .funs = mean)
}# There's no TS defined in the previous code
if(FALSE){
TS %>%
tidyr::spread(data = ., HOST, Result)
HxDF %>%
filter(pig.herd !=0)
}Weighted linear combination
HxDF %>%
mutate(density_s = scales::rescale(x = density, to = c(0.1,0.6))) %>%
ggplot() + geom_histogram(aes(density_s))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Variables:
DET: detection method:
- PS- Passive surveillance.
- RZ- Detection in a site from surveillance or protection zone.
- TR- Detection via tracing.
- AS- Wild boar carcass via active searching
- NAS- non-infected carcass via active searching.
- PT- detection of in a hunted boar.
- NT- non-infected hunted boar.
# Export data:
Hx %>%
filter(!is.na(Pop) | !is.na(E_WB)) %>% # Select only the cells with a WB or PH population
mutate(Farms = ifelse(is.na(Farms), 0.0001, Farms), # If there is no PH pop, set a very small number for the model
density_s = scales::rescale(x = density, to = c(0.1,0.6)), # Score based on the density
WB_score = scales::rescale(x = Forest, to = c(0,.5)) + # score based on the forst cover and EB density
scales::rescale(x = WBd, to = c(0,.5))) %>%
write_sf(here("Data/Period_1/out/Hx.shp"))## Warning in abbreviate_shapefile_names(obj): Field names abbreviated for ESRI
## Shapefile driverTSp_s %>%
filter(ph_cases != 0)## # A tibble: 3 x 4
## idhex cases wb_cases ph_cases
## <chr> <dbl> <dbl> <dbl>
## 1 591 34 33 1
## 2 623 2 1 1
## 3 625 91 90 1Mov %>%
filter(source == 785)## [1] date source source.type dest dest.type qty
## <0 rows> (or 0-length row.names)Pig cases does not seem to be asociated with movements (very few movements from the affected).
wild boar cases on those hexagons are not particularly large
HxDF %>%
arrange(desc(wb_cases)) # wild boar cases seem to happen in forest areas## idhex Farms Pop is_outdoor is_commercial multisite B BF F density
## 1 589 1 12 1 0 0 1 0 0 12.000000
## 2 625 1 3 1 0 0 0 0 1 3.000000
## 3 587 3 4408 0 3 41 2 1 0 1469.333333
## 4 556 1 221 0 1 122 0 0 1 221.000000
## 5 627 2 11 1 0 0 0 0 2 5.500000
## 6 591 3 1141 1 2 0 0 1 2 380.333333
## 7 663 2 5105 1 1 0 1 0 1 2552.500000
## 8 517 5 1363 0 3 0 0 1 4 272.600000
## 9 553 2 1334 0 2 75 0 1 1 667.000000
## 10 665 2 2752 0 1 98 0 0 2 1376.000000
## 11 515 2 263 1 1 0 1 0 1 131.500000
## 12 660 4 204 1 1 0 0 1 3 51.000000
## 13 481 4 6068 1 4 0 1 2 1 1517.000000
## 14 558 1 36 0 0 0 1 0 0 36.000000
## 15 623 2 1445 1 2 17 0 0 2 722.500000
## 16 2 1 126 1 1 0 0 0 1 126.000000
## 17 4 3 9139 2 3 55 1 1 1 3046.333333
## 18 6 2 14466 0 2 42 1 0 1 7233.000000
## 19 7 8 3001 3 6 0 0 3 5 375.125000
## 20 8 5 3934 2 3 0 0 2 3 786.800000
## 21 9 1 3397 0 1 56 1 0 0 3397.000000
## 22 11 5 997 2 4 0 0 2 3 199.400000
## 23 12 3 1777 0 2 0 0 0 3 592.333333
## 24 13 2 1130 0 1 83 0 1 1 565.000000
## 25 16 3 11509 0 3 61 1 1 1 3836.333333
## 26 17 3 11509 0 3 61 1 1 1 3836.333333
## 27 18 9 6200 0 9 141 1 5 3 688.888889
## 28 19 9 6200 0 9 141 1 5 3 688.888889
## 29 20 5 7585 2 5 3 2 1 2 1517.000000
## 30 21 8 11595 1 7 58 1 1 6 1449.375000
## 31 22 4 1957 0 4 0 1 2 1 489.250000
## 32 23 5 1832 1 3 90 1 1 3 366.400000
## 33 24 11 7061 1 9 32 1 5 5 641.909091
## 34 25 12 11923 2 10 81 3 3 6 993.583333
## 35 26 1 1556 1 1 48 0 0 1 1556.000000
## 36 27 1 1156 0 1 9 0 1 0 1156.000000
## 37 28 4 5624 3 4 0 0 1 3 1406.000000
## 38 29 2 5962 0 1 119 1 0 1 2981.000000
## 39 30 2 5962 0 1 119 1 0 1 2981.000000
## 40 33 2 1619 0 2 79 0 0 2 809.500000
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## 213 NA NA NA 1.962511e-06 29.437669 0.209480122
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## 229 NA NA NA 1.074020e-06 16.110296 0.745038168
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## 234 0.37272727 0.000000000 13.0 5.771734e-06 86.576015 0.845801527
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## 241 0.27272727 0.000000000 122.0 1.154093e-06 17.311390 0.873323398
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## 246 0.68181818 0.000000000 44.5 1.585529e-06 23.782929 0.281345566
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## 249 0.14545455 0.000000000 4.0 2.218668e-06 33.280019 0.573394495
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## 255 NA NA NA 3.493824e-06 52.407357 0.294656489
## 256 0.30909091 0.000000000 22.0 3.560717e-06 53.410758 0.340458015
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## 259 1.22727273 0.000000000 54.0 1.123688e-06 16.855313 0.696183206
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## 263 0.05454545 0.000000000 2.0 1.074020e-06 16.110296 0.856488550
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## 265 0.14545455 0.000000000 24.0 1.851876e-06 27.778140 0.932721713
## 266 0.14545455 0.000000000 24.0 1.851876e-06 27.778140 0.932721713
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## 268 0.14545455 0.000000000 67.0 5.726492e-06 85.897373 0.593893130
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## 271 0.17272727 0.000000000 6.0 5.824611e-06 87.369165 0.880733945
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## 288 0.09090909 0.000000000 6.0 2.218668e-06 33.280019 0.438837920
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## 290 0.04545455 0.000000000 0.0 2.239283e-06 33.589247 0.802752294
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## 294 NA NA NA 1.074020e-06 16.110296 0.012024048
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## 296 0.67272727 0.000000000 44.0 1.074020e-06 16.110296 0.834862385
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## 311 0.31818182 0.000000000 19.5 2.199575e-06 32.993620 0.868300153
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## 625 NA NA NA 8.655318e-07 12.982976 0.274809160
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## 629 NA NA NA 1.399704e-06 20.995563 0.645801527
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## 647 0.61818182 0.000000000 123.0 1.628910e-06 24.433645 0.476190476
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## 649 NA NA NA 5.413306e-07 8.119959 0.717557252
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## 653 0.10000000 0.000000000 0.5 7.480134e-07 11.220201 0.709480122
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## 657 NA NA NA 6.898400e-07 10.347601 0.621374046
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## 659 NA NA NA 7.625250e-07 11.437875 0.391437309
## 660 NA NA NA 7.625250e-07 11.437875 0.391437309
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## 665 NA NA NA 5.709669e-07 8.564503 0.654434251
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## 667 1.48181818 0.000000000 31.0 2.261434e-06 33.921505 0.620795107
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## 669 NA NA NA 4.896965e-06 73.454468 0.325190840
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## 671 NA NA NA 4.896965e-06 73.454468 0.387786260
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## 673 NA NA NA 2.008984e-06 30.134759 0.661068702
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## 675 0.05454545 0.000000000 3.0 1.374381e-06 20.615717 0.307339450
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## 677 0.08181818 0.000000000 2.0 1.050785e-06 15.761777 0.245801527
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## 680 0.10000000 0.000000000 5.5 8.166039e-07 12.249058 0.429663609
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## 687 0.14545455 0.000000000 3.0 5.413306e-07 8.119959 0.567278287
## 688 0.14545455 0.000000000 2.0 5.413306e-07 8.119959 0.903816794
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## 699 0.04545455 0.000000000 0.5 3.518094e-06 52.771409 0.616207951
## 700 0.04545455 0.000000000 0.5 3.518094e-06 52.771409 0.616207951
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## 705 NA NA NA 1.261802e-06 18.927033 0.288990826
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## 708 0.15454545 0.000000000 2.0 8.166039e-07 12.249058 0.497709924
## 709 NA NA NA 8.166039e-07 12.249058 0.099236641
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## 712 NA NA NA 5.413306e-07 8.119959 0.635114504
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## 714 0.10000000 0.000000000 4.0 5.413306e-07 8.119959 0.779816514
## 715 0.05454545 0.000000000 2.0 5.416999e-07 8.125499 0.679938744
## 716 0.05454545 0.000000000 2.0 5.416999e-07 8.125499 0.679938744
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## 718 NA NA NA 5.442762e-07 8.164143 0.512232416
## 719 NA NA NA 5.443453e-07 8.165180 0.636641221
## 720 1.03636364 0.000000000 38.0 5.443453e-07 8.165180 0.700763359
## 721 NA NA NA 5.443453e-07 8.165180 0.582568807
## 722 NA NA NA 5.443453e-07 8.165180 0.614678899
## 723 0.03636364 0.000000000 1.0 1.242093e-06 18.631402 0.473282443
## 724 0.03636364 0.000000000 1.0 1.242093e-06 18.631402 0.473282443
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## 726 NA NA NA 4.823867e-06 72.358006 0.221374046
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## 728 0.07272727 0.000000000 3.0 4.676467e-06 70.147004 0.624427481
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## 731 NA NA NA 8.208682e-07 12.313023 0.639143731
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## 733 NA NA NA 8.336613e-07 12.504919 0.266055046
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## 736 0.10000000 0.000000000 243.0 8.166099e-07 12.249148 0.484709480
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## 738 NA NA NA 8.183638e-07 12.275458 0.515290520
## 739 NA NA NA 8.205308e-07 12.307962 0.477064220
## 740 0.05454545 0.000000000 3.0 5.413306e-07 8.119959 0.587155963
## 741 0.26363636 0.000000000 21.5 5.413306e-07 8.119959 0.776758410
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## 747 NA NA NA 5.272770e-07 7.909156 0.463302752
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## 749 0.06363636 0.000000000 24.0 5.442163e-07 8.163244 0.581039755
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## 753 0.09090909 0.000000000 132.0 5.443453e-07 8.165180 0.531393568
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## 755 NA NA NA 5.496266e-07 8.244399 0.425727412
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## 757 NA NA NA 5.496185e-07 8.244278 0.490825688
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## 759 1.19090909 0.000000000 9.5 2.789446e-06 41.841693 0.359877489
## 760 1.19090909 0.000000000 9.5 2.789446e-06 41.841693 0.359877489
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## 762 0.07272727 0.000000000 6.0 4.731016e-06 70.965245 0.526865672
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## 765 0.17272727 0.000000000 40.0 2.352619e-06 35.289278 0.742725881
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## 767 NA NA NA 1.347861e-06 20.217916 0.668711656
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## 775 0.09090909 0.000000000 11.0 8.193059e-07 12.289588 0.642201835
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## 778 0.06363636 0.000000000 8.0 8.205308e-07 12.307962 0.492354740
## 779 NA NA NA 8.205308e-07 12.307962 0.470948012
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## 781 0.05454545 0.000000000 8.0 4.895580e-07 7.343370 0.435779817
## 782 0.05454545 0.000000000 8.0 4.895580e-07 7.343370 0.435779817
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## 784 0.07272727 0.000000000 13.0 3.442419e-07 5.163628 0.821100917
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## 787 0.05454545 0.000000000 12.0 1.630525e-07 2.445787 0.692189893
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## 789 NA NA NA 3.145284e-07 4.717926 0.641653905
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## 791 0.09090909 0.000000000 39.0 4.275373e-07 6.413060 0.438837920
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## 793 NA NA NA 3.217425e-07 4.826137 0.259541985
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## 795 NA NA NA 2.864190e-07 4.296284 0.464831804
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## 797 0.11818182 0.000000000 47.5 2.720020e-06 40.800293 0.513761468
## 798 0.11818182 0.000000000 47.5 2.720020e-06 40.800293 0.513761468
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## 802 NA NA NA 3.419118e-06 51.286766 0.416793893
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## 806 NA NA NA 2.921892e-06 43.828384 0.613740458
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## 808 0.27272727 0.000000000 64.0 8.872840e-07 13.309260 0.775572519
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## 810 0.30909091 0.000000000 48.0 8.200101e-07 12.300152 0.760305344
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## 812 0.13636364 0.000000000 145.0 8.166039e-07 12.249058 0.607033639
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## 815 NA NA NA 8.168020e-07 12.252030 0.359327217
## 816 NA NA NA 8.166939e-07 12.250409 0.276758410
## 817 NA NA NA 8.166939e-07 12.250409 0.276758410
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## 819 NA NA NA 8.204285e-07 12.306428 0.294027565
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## 821 NA NA NA 8.205308e-07 12.307962 0.689602446
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## 823 NA NA NA 8.205308e-07 12.307962 0.730474732
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## 825 NA NA NA 2.330039e-07 3.495059 0.357798165
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## 828 0.06363636 0.000000000 0.0 1.565664e-07 2.348496 0.707951070
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## 831 NA NA NA 1.565664e-07 2.348496 0.396946565
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## 833 NA NA NA 3.442827e-07 5.164241 0.284403670
## 834 NA NA NA 3.442827e-07 5.164241 0.284403670
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## 836 NA NA NA 3.969555e-06 59.543319 0.555045872
## 837 0.04545455 0.000000000 5.5 3.993019e-06 59.895287 0.580152672
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## 844 NA NA NA 8.166039e-07 12.249058 0.638167939
## 845 0.70000000 0.000000000 57.0 8.166039e-07 12.249058 0.668702290
## 846 0.05454545 0.000000000 5.0 8.166039e-07 12.249058 0.732415902
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## 857 NA NA NA 8.205308e-07 12.307962 0.605504587
## 858 0.52727273 0.000000000 50.0 8.205308e-07 12.307962 0.775229358
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## 874 NA NA NA 4.273054e-06 64.095816 0.840978593
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## 876 0.21818182 0.000000000 147.0 4.171918e-06 62.578770 0.604900459
## 877 0.21818182 0.000000000 147.0 4.171918e-06 62.578770 0.604900459
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## 881 NA NA NA 8.166039e-07 12.249058 0.641221374
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## 887 0.05454545 0.000000000 12.0 8.342393e-07 12.513589 0.636641221
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## 889 NA NA NA 8.205308e-07 12.307962 0.376146789
## 890 NA NA NA 8.205308e-07 12.307962 0.366972477
## 891 0.08181818 0.000000000 6.0 8.205308e-07 12.307962 0.293577982
## 892 0.08181818 0.000000000 376.5 8.205308e-07 12.307962 0.528244275
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## 894 NA NA NA 8.205308e-07 12.307962 0.133027523
## 895 NA NA NA 1.565664e-07 2.348496 0.466360856
## 896 NA NA NA 1.565664e-07 2.348496 0.659541985
## 897 0.97272727 0.000000000 30.0 1.565664e-07 2.348496 0.527522936
## 898 0.49090909 0.000000000 51.0 1.565664e-07 2.348496 0.450229709
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## 901 0.09090909 0.000000000 29.0 3.993019e-06 59.895287 0.383792049
## 902 0.06363636 0.000000000 1.5 3.993019e-06 59.895287 0.746177370
## 903 0.07272727 0.000000000 28.0 3.997414e-06 59.961207 0.819847328
## 904 0.07272727 0.000000000 28.0 3.997414e-06 59.961207 0.819847328
## 905 0.46363636 0.000000000 29.0 4.335800e-06 65.036993 0.796946565
## 906 0.46363636 0.000000000 29.0 4.335800e-06 65.036993 0.796946565
## 907 0.12727273 0.000000000 2.0 4.352829e-06 65.292430 0.563988095
## 908 NA NA NA 4.352829e-06 65.292430 0.204580153
## 909 0.04545455 0.000000000 4.0 3.683375e-06 55.250631 0.648854962
## 910 0.04545455 0.000000000 4.0 3.683375e-06 55.250631 0.648854962
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## 912 NA NA NA 8.656642e-07 12.984963 0.629969419
## 913 NA NA NA 8.656642e-07 12.984963 0.629969419
## 914 0.54545455 0.000000000 54.0 8.331532e-07 12.497298 0.605504587
## 915 0.54545455 0.000000000 54.0 8.331532e-07 12.497298 0.605504587
## 916 0.11818182 0.000000000 1.0 1.114666e-06 16.719993 0.431192661
## 917 0.11818182 0.000000000 1.0 1.114666e-06 16.719993 0.431192661
## 918 0.11818182 0.000000000 1.0 1.114666e-06 16.719993 0.431192661
## 919 0.87272727 0.000000000 33.0 8.584776e-07 12.877164 0.482442748
## 920 0.87272727 0.000000000 33.0 8.584776e-07 12.877164 0.482442748
## 921 NA NA NA 8.221013e-07 12.331520 0.417431193
## 922 NA NA NA 8.221013e-07 12.331520 0.417431193
## 923 NA NA NA 8.274380e-07 12.411571 0.326186830
## 924 NA NA NA 8.274380e-07 12.411571 0.326186830
## 925 0.06363636 0.000000000 2.0 8.225793e-07 12.338690 0.244648318
## 926 0.06363636 0.000000000 2.0 8.225793e-07 12.338690 0.244648318
## 927 0.27272727 0.000000000 93.0 8.205308e-07 12.307962 0.487022901
## 928 0.04545455 0.000000000 4.5 8.206048e-07 12.309073 0.648854962
## 929 0.04545455 0.000000000 4.5 8.206048e-07 12.309073 0.648854962
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## 931 0.80909091 0.000000000 36.0 1.565664e-07 2.348496 0.649847095
## 932 NA NA NA 1.565664e-07 2.348496 0.577099237
## 933 NA NA NA 1.565664e-07 2.348496 0.393893130
## 934 0.05454545 0.000000000 1.0 1.565664e-07 2.348496 0.836641221
## 935 0.29090909 0.000000000 79.0 1.565664e-07 2.348496 0.743511450
## 936 NA NA NA 1.565664e-07 2.348496 0.395419847
## 937 NA NA NA 3.470933e-06 52.063996 0.472477064
## 938 NA NA NA 3.470933e-06 52.063996 0.472477064
## 939 NA NA NA 3.993019e-06 59.895287 0.770642202
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## 941 NA NA NA 4.190529e-06 62.857939 0.629969419
## 942 NA NA NA 4.190529e-06 62.857939 0.629969419
## 943 0.06363636 0.000000000 4.0 4.352829e-06 65.292430 0.755351682
## 944 0.07272727 0.000000000 0.0 4.347948e-06 65.219220 0.467075038
## 945 0.07272727 0.000000000 0.0 4.347948e-06 65.219220 0.467075038
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## 947 NA NA NA 2.539990e-06 38.099853 0.614678899
## 948 0.04545455 0.000000000 1.0 1.256279e-06 18.844192 0.705343511
## 949 0.04545455 0.000000000 1.0 1.256279e-06 18.844192 0.705343511
## 950 0.04545455 0.000000000 1.0 1.256279e-06 18.844192 0.705343511
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## 952 NA NA NA 9.562590e-07 14.343885 0.636641221
## 953 NA NA NA 1.163606e-06 17.454094 0.301223242
## 954 NA NA NA 1.163606e-06 17.454094 0.301223242
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## 956 NA NA NA 1.128792e-06 16.931887 0.454128440
## 957 0.39090909 0.000000000 25.0 8.310987e-07 12.466480 0.607633588
## 958 0.39090909 0.000000000 25.0 8.310987e-07 12.466480 0.607633588
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## 960 NA NA NA 8.419393e-07 12.629089 0.442748092
## 961 NA NA NA 8.423484e-07 12.635226 0.383206107
## 962 NA NA NA 8.423484e-07 12.635226 0.383206107
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## 966 NA NA NA 8.360758e-07 12.541137 0.557251908
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## 968 0.27272727 0.000000000 115.0 1.565664e-07 2.348496 0.643730887
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## 970 NA NA NA 1.565664e-07 2.348496 0.879204893
## 971 NA NA NA 2.263019e-07 3.394529 0.477794793
## 972 NA NA NA 2.263019e-07 3.394529 0.477794793
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## 974 NA NA NA 1.378289e-06 20.674329 0.324159021
## 975 NA NA NA 1.378289e-06 20.674329 0.324159021
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## 977 NA NA NA 3.874721e-06 58.120820 0.508422665
## 978 0.20909091 0.000000000 70.5 3.993019e-06 59.895287 0.707503828
## 979 NA NA NA 4.111486e-06 61.672295 0.800918836
## 980 NA NA NA 4.111486e-06 61.672295 0.800918836
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## 982 0.10909091 0.000000000 4.0 4.163121e-06 62.446817 0.741071429
## 983 0.10909091 0.000000000 4.0 4.163121e-06 62.446817 0.741071429
## 984 0.20909091 0.000000000 9.0 1.492248e-06 22.383713 0.804281346
## 985 0.20909091 0.000000000 9.0 1.492248e-06 22.383713 0.804281346
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## 987 0.06363636 0.000000000 4.0 1.165742e-06 17.486125 0.663093415
## 988 NA NA NA 1.165742e-06 17.486125 0.238897397
## 989 0.09090909 0.000000000 153.0 1.165742e-06 17.486125 0.512232416
## 990 NA NA NA 9.920183e-07 14.880274 0.396024465
## 991 NA NA NA 9.920183e-07 14.880274 0.396024465
## 992 NA NA NA 9.920183e-07 14.880274 0.396024465
## 993 NA NA NA 8.428597e-07 12.642896 0.576452599
## 994 NA NA NA 8.428597e-07 12.642896 0.340978593
## 995 0.10909091 0.000000000 1.0 8.422110e-07 12.633165 0.434250765
## 996 0.10909091 0.000000000 1.0 8.422110e-07 12.633165 0.434250765
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## 998 NA NA NA 8.409136e-07 12.613704 0.486238532
## 999 NA NA NA 8.428597e-07 12.642896 0.241221374
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## 1001 NA NA NA 2.543241e-07 3.814861 0.701378254
## 1002 0.08181818 0.000000000 2.0 1.565664e-07 2.348496 0.655963303
## 1003 NA NA NA 3.757262e-07 5.635893 0.631498471
## 1004 NA NA NA 3.757262e-07 5.635893 0.631498471
## 1005 0.03636364 0.000000000 12.0 6.542419e-07 9.813628 0.821100917
## 1006 0.03636364 0.000000000 12.0 6.542419e-07 9.813628 0.821100917
## 1007 0.27272727 0.000000000 47.0 7.318610e-07 10.977915 0.545871560
## 1008 0.27272727 0.000000000 47.0 7.318610e-07 10.977915 0.545871560
## 1009 0.59090909 0.000000000 19.0 8.973723e-07 13.460585 0.677370031
## 1010 0.59090909 0.000000000 19.0 8.973723e-07 13.460585 0.677370031
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## 1012 NA NA NA 2.216540e-06 33.248102 0.264525994
## 1013 0.15454545 0.000000000 72.0 3.993019e-06 59.895287 0.781345566
## 1014 0.05454545 0.000000000 3.0 3.999062e-06 59.985926 0.865648855
## 1015 0.05454545 0.000000000 3.0 3.999062e-06 59.985926 0.865648855
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## 1017 NA NA NA 4.307715e-06 64.615724 0.467889908
## 1018 NA NA NA 4.352829e-06 65.292430 0.526717557
## 1019 0.06363636 0.000000000 0.5 2.625476e-06 39.382143 0.847328244
## 1020 0.06363636 0.000000000 0.5 2.625476e-06 39.382143 0.847328244
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## 1022 0.05454545 0.000000000 1.0 1.165742e-06 17.486125 0.792048930
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## 1024 0.89090909 0.000000000 96.0 1.165742e-06 17.486125 0.376146789
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## 1026 NA NA NA 1.141550e-06 17.123253 0.382262997
## 1027 NA NA NA 8.547086e-07 12.820629 0.581039755
## 1028 NA NA NA 8.547086e-07 12.820629 0.581039755
## 1029 NA NA NA 8.428597e-07 12.642896 0.622324159
## 1030 NA NA NA 8.428597e-07 12.642896 0.532110092
## 1031 0.06363636 0.000000000 1.0 8.428597e-07 12.642896 0.417431193
## 1032 NA NA NA 8.428597e-07 12.642896 0.392036753
## 1033 0.10000000 0.000000000 6.0 8.428597e-07 12.642896 0.425727412
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## 1035 NA NA NA 9.009345e-07 13.514017 0.421374046
## 1036 NA NA NA 6.291298e-07 9.436947 0.862385321
## 1037 NA NA NA 6.291298e-07 9.436947 0.862385321
## 1038 NA NA NA 5.229743e-07 7.844614 0.776758410
## 1039 NA NA NA 5.229743e-07 7.844614 0.776758410
## 1040 NA NA NA 9.030796e-07 13.546194 0.762996942
## 1041 0.07272727 0.000000000 12.0 9.030796e-07 13.546194 0.782874618
## 1042 NA NA NA 9.030796e-07 13.546194 0.877488515
## 1043 NA NA NA 9.881238e-07 14.821857 0.723241590
## 1044 NA NA NA 9.881238e-07 14.821857 0.723241590
## 1045 0.20909091 0.000000000 40.5 3.937951e-06 59.069264 0.647328244
## 1046 0.20909091 0.000000000 40.5 3.937951e-06 59.069264 0.647328244
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## 1049 0.45454545 0.000000000 46.0 4.309451e-06 64.641770 0.438837920
## 1050 0.45454545 0.000000000 46.0 4.309451e-06 64.641770 0.438837920
## 1051 0.44545455 0.000000000 24.0 4.352829e-06 65.292430 0.607142857
## 1052 0.09090909 0.000000000 65.0 1.268056e-06 19.020838 0.799694190
## 1053 0.09090909 0.000000000 65.0 1.268056e-06 19.020838 0.799694190
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## 1055 NA NA NA 1.165742e-06 17.486125 0.545871560
## 1056 0.14545455 0.000000000 3.0 1.165742e-06 17.486125 0.388379205
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## 1058 NA NA NA 1.160797e-06 17.411956 0.286370597
## 1059 NA NA NA 1.160797e-06 17.411956 0.286370597
## 1060 0.07272727 0.000000000 11.0 8.428597e-07 12.642896 0.376146789
## 1061 0.05454545 0.000000000 1.0 8.428597e-07 12.642896 0.437308869
## 1062 0.38181818 0.000000000 68.0 8.428597e-07 12.642896 0.379204893
## 1063 1.67272727 0.000000000 32.0 8.428597e-07 12.642896 0.331297710
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## 1065 NA NA NA 9.030796e-07 13.546194 0.695718654
## 1066 0.37272727 0.000000000 106.0 9.030796e-07 13.546194 0.900611621
## 1067 0.37272727 0.000000000 106.0 9.030796e-07 13.546194 0.900611621
## 1068 0.16363636 0.000000000 46.0 9.030796e-07 13.546194 0.856488550
## 1069 NA NA NA 9.030796e-07 13.546194 0.893129771
## 1070 0.06363636 0.000000000 2.0 9.030796e-07 13.546194 0.862595420
## 1071 0.29090909 0.000000000 41.0 9.030796e-07 13.546194 0.914503817
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## 1073 NA NA NA 9.456017e-07 14.184026 0.867175573
## 1074 NA NA NA 9.030796e-07 13.546194 0.262595420
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## 1076 NA NA NA 4.352829e-06 65.292430 0.157492355
## 1077 NA NA NA 1.870849e-06 28.062741 0.123664122
## 1078 NA NA NA 1.870849e-06 28.062741 0.123664122
## 1079 NA NA NA 1.165742e-06 17.486125 0.849923430
## 1080 0.13636364 0.000000000 14.5 1.165742e-06 17.486125 0.768759571
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## 1082 0.10909091 0.000000000 4.0 1.165742e-06 17.486125 0.158778626
## 1083 NA NA NA 1.165742e-06 17.486125 0.267175573
## 1084 NA NA NA 1.165742e-06 17.486125 0.216793893
## 1085 NA NA NA 8.428597e-07 12.642896 0.361832061
## 1086 0.09090909 0.000000000 3.0 8.428597e-07 12.642896 0.366412214
## 1087 NA NA NA 8.428597e-07 12.642896 0.371559633
## 1088 NA NA NA 8.428597e-07 12.642896 0.039816233
## 1089 0.59090909 0.000000000 61.0 9.030796e-07 13.546194 0.645801527
## 1090 NA NA NA 9.030796e-07 13.546194 0.845801527
## 1091 0.09090909 0.000000000 6.0 9.030796e-07 13.546194 0.865237366
## 1092 NA NA NA 9.030796e-07 13.546194 0.862174579
## 1093 NA NA NA 9.030796e-07 13.546194 0.906870229
## 1094 0.20909091 0.000000000 5.5 9.030796e-07 13.546194 0.857798165
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## 1096 NA NA NA 9.030796e-07 13.546194 0.877488515
## 1097 NA NA NA 9.030796e-07 13.546194 0.801526718
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## 1103 0.07272727 0.000000000 1.0 9.030796e-07 13.546194 0.512232416
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## 1105 0.05454545 0.000000000 9.0 9.030796e-07 13.546194 0.845801527
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## 1107 NA NA NA 9.030796e-07 13.546194 0.677862595
## 1108 0.09090909 0.000000000 10.0 9.030796e-07 13.546194 0.818320611
## 1109 NA NA NA 9.030796e-07 13.546194 0.311450382
## 1110 0.85454545 0.000000000 80.0 9.030796e-07 13.546194 0.475535168
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## 1112 0.60909091 0.000000000 20.5 9.030796e-07 13.546194 0.676335878
## 1113 NA NA NA 9.030796e-07 13.546194 0.539755352
## 1114 NA NA NA 9.030796e-07 13.546194 0.539050536
## 1115 NA NA NA 9.030796e-07 13.546194 0.323124043
## 1116 NA NA NA 9.030796e-07 13.546194 0.220843672
## 1117 NA NA NA 9.030796e-07 13.546194 0.476426799
## Forest Urban cases wb_cases ph_cases
## 1 0.894495413 0.000000000 111 111 0
## 2 0.802752294 0.004587156 91 90 1
## 3 0.834862385 0.003058104 46 46 0
## 4 0.668702290 0.007633588 42 42 0
## 5 0.885496183 0.012213740 34 34 0
## 6 0.763803681 0.010736196 34 33 1
## 7 0.670229008 0.003053435 18 18 0
## 8 0.923664122 0.010687023 12 12 0
## 9 0.908256881 0.004587156 10 10 0
## 10 0.767228178 0.001531394 4 4 0
## 11 0.932824427 0.003053435 3 3 0
## 12 0.343511450 0.010687023 3 3 0
## 13 0.516819572 0.111620795 1 1 0
## 14 0.462595420 0.029007634 1 1 0
## 15 0.630534351 0.007633588 2 1 1
## 16 0.149758454 0.000000000 NA NA NA
## 17 0.389578164 0.004962779 NA NA NA
## 18 0.189312977 0.004580153 NA NA NA
## 19 0.319083969 0.044274809 NA NA NA
## 20 0.497709924 0.004580153 NA NA NA
## 21 0.648854962 0.000000000 NA NA NA
## 22 0.617737003 0.039755352 NA NA NA
## 23 0.400611621 0.022935780 NA NA NA
## 24 0.245801527 0.035114504 NA NA NA
## 25 0.250381679 0.001526718 NA NA NA
## 26 0.250381679 0.001526718 NA NA NA
## 27 0.399693721 0.001531394 NA NA NA
## 28 0.399693721 0.001531394 NA NA NA
## 29 0.670149254 0.016417910 NA NA NA
## 30 0.654961832 0.007633588 NA NA NA
## 31 0.670229008 0.018320611 NA NA NA
## 32 0.847094801 0.009174312 NA NA NA
## 33 0.113149847 0.081039755 NA NA NA
## 34 0.088549618 0.085496183 NA NA NA
## 35 0.044274809 0.000000000 NA NA NA
## 36 0.000000000 0.000000000 NA NA NA
## 37 0.091603053 0.000000000 NA NA NA
## 38 0.056661562 0.019908116 NA NA NA
## 39 0.056661562 0.019908116 NA NA NA
## 40 0.105666156 0.000000000 NA NA NA
## 41 0.489296636 0.000000000 NA NA NA
## 42 0.519083969 0.000000000 NA NA NA
## 43 0.749618321 0.009160305 NA NA NA
## 44 0.653435115 0.000000000 NA NA NA
## 45 0.653435115 0.000000000 NA NA NA
## 46 0.653435115 0.000000000 NA NA NA
## 47 0.780152672 0.000000000 NA NA NA
## 48 0.780152672 0.000000000 NA NA NA
## 49 0.780152672 0.000000000 NA NA NA
## 50 0.609160305 0.003053435 NA NA NA
## 51 0.609160305 0.003053435 NA NA NA
## 52 0.609160305 0.016793893 NA NA NA
## 53 0.171516080 0.059724349 NA NA NA
## 54 0.001531394 0.251148545 NA NA NA
## 55 0.001526718 0.016793893 NA NA NA
## 56 0.014056225 0.004016064 NA NA NA
## 57 0.173047473 0.015313936 NA NA NA
## 58 0.319083969 0.001526718 NA NA NA
## 59 0.528244275 0.007633588 NA NA NA
## 60 0.259541985 0.001526718 NA NA NA
## 61 0.259541985 0.001526718 NA NA NA
## 62 0.476335878 0.016793893 NA NA NA
## 63 0.138931298 0.000000000 NA NA NA
## 64 0.138931298 0.000000000 NA NA NA
## 65 0.329770992 0.000000000 NA NA NA
## 66 0.699236641 0.003053435 NA NA NA
## 67 0.637614679 0.004587156 NA NA NA
## 68 0.686544343 0.022935780 NA NA NA
## 69 0.621374046 0.030534351 NA NA NA
## 70 0.687595712 0.003062787 NA NA NA
## 71 0.687595712 0.003062787 NA NA NA
## 72 0.759701493 0.002985075 NA NA NA
## 73 0.759701493 0.002985075 NA NA NA
## 74 0.859327217 0.010703364 NA NA NA
## 75 0.859327217 0.010703364 NA NA NA
## 76 0.619266055 0.012232416 NA NA NA
## 77 0.619266055 0.012232416 NA NA NA
## 78 0.559633028 0.015290520 NA NA NA
## 79 0.559633028 0.015290520 NA NA NA
## 80 0.365443425 0.064220183 NA NA NA
## 81 0.365443425 0.064220183 NA NA NA
## 82 0.041221374 0.062595420 NA NA NA
## 83 0.041221374 0.062595420 NA NA NA
## 84 0.510703364 0.007645260 NA NA NA
## 85 0.460244648 0.009174312 NA NA NA
## 86 0.293577982 0.001529052 NA NA NA
## 87 0.674311927 0.015290520 NA NA NA
## 88 0.669218989 0.006125574 NA NA NA
## 89 0.669218989 0.006125574 NA NA NA
## 90 0.733944954 0.016819572 NA NA NA
## 91 0.725190840 0.006106870 NA NA NA
## 92 0.725190840 0.006106870 NA NA NA
## 93 0.608562691 0.006116208 NA NA NA
## 94 0.689602446 0.003058104 NA NA NA
## 95 0.654961832 0.004580153 NA NA NA
## 96 0.595419847 0.035114504 NA NA NA
## 97 0.609160305 0.038167939 NA NA NA
## 98 0.665648855 0.010687023 NA NA NA
## 99 0.665648855 0.010687023 NA NA NA
## 100 0.500763359 0.027480916 NA NA NA
## 101 0.777099237 0.000000000 NA NA NA
## 102 0.725190840 0.001526718 NA NA NA
## 103 0.753822630 0.004587156 NA NA NA
## 104 0.753822630 0.004587156 NA NA NA
## 105 0.506116208 0.010703364 NA NA NA
## 106 0.506116208 0.010703364 NA NA NA
## 107 0.240480962 0.002004008 NA NA NA
## 108 0.487022901 0.039694656 NA NA NA
## 109 0.528244275 0.000000000 NA NA NA
## 110 0.573394495 0.010703364 NA NA NA
## 111 0.570336391 0.027522936 NA NA NA
## 112 0.570336391 0.027522936 NA NA NA
## 113 0.555045872 0.061162080 NA NA NA
## 114 0.555045872 0.061162080 NA NA NA
## 115 0.525993884 0.016819572 NA NA NA
## 116 0.570336391 0.012232416 NA NA NA
## 117 0.570336391 0.012232416 NA NA NA
## 118 0.383792049 0.033639144 NA NA NA
## 119 0.383792049 0.033639144 NA NA NA
## 120 0.505359877 0.004594181 NA NA NA
## 121 0.441041348 0.018376723 NA NA NA
## 122 0.454823890 0.018376723 NA NA NA
## 123 0.376722818 0.026033691 NA NA NA
## 124 0.376722818 0.026033691 NA NA NA
## 125 0.432835821 0.004477612 NA NA NA
## 126 0.520673813 0.027565084 NA NA NA
## 127 0.759571210 0.000000000 NA NA NA
## 128 0.675344564 0.004594181 NA NA NA
## 129 0.596330275 0.009174312 NA NA NA
## 130 0.159021407 0.107033639 NA NA NA
## 131 0.192660550 0.006116208 NA NA NA
## 132 0.347626340 0.091883614 NA NA NA
## 133 0.304747320 0.045941807 NA NA NA
## 134 0.357798165 0.070336391 NA NA NA
## 135 0.264525994 0.051987768 NA NA NA
## 136 0.264525994 0.051987768 NA NA NA
## 137 0.246177370 0.087155963 NA NA NA
## 138 0.255742726 0.050535988 NA NA NA
## 139 0.397553517 0.021406728 NA NA NA
## 140 0.397553517 0.021406728 NA NA NA
## 141 0.313455657 0.006116208 NA NA NA
## 142 0.313455657 0.006116208 NA NA NA
## 143 0.163358779 0.001526718 NA NA NA
## 144 0.163358779 0.001526718 NA NA NA
## 145 0.111450382 0.062595420 NA NA NA
## 146 0.111450382 0.062595420 NA NA NA
## 147 0.276335878 0.001526718 NA NA NA
## 148 0.276335878 0.001526718 NA NA NA
## 149 0.132824427 0.009160305 NA NA NA
## 150 0.132824427 0.009160305 NA NA NA
## 151 0.222900763 0.024427481 NA NA NA
## 152 0.341984733 0.111450382 NA NA NA
## 153 0.427480916 0.021374046 NA NA NA
## 154 0.218320611 0.036641221 NA NA NA
## 155 0.339449541 0.096330275 NA NA NA
## 156 0.328746177 0.067278287 NA NA NA
## 157 0.039694656 0.001526718 NA NA NA
## 158 0.152905199 0.240061162 NA NA NA
## 159 0.325688073 0.022935780 NA NA NA
## 160 0.267993874 0.026033691 NA NA NA
## 161 0.267993874 0.026033691 NA NA NA
## 162 0.267993874 0.026033691 NA NA NA
## 163 0.232061069 0.015267176 NA NA NA
## 164 0.232061069 0.015267176 NA NA NA
## 165 0.232061069 0.015267176 NA NA NA
## 166 0.180152672 0.009160305 NA NA NA
## 167 0.129770992 0.027480916 NA NA NA
## 168 0.061068702 0.036641221 NA NA NA
## 169 0.079389313 0.010687023 NA NA NA
## 170 0.079389313 0.010687023 NA NA NA
## 171 0.123853211 0.032110092 NA NA NA
## 172 0.123853211 0.032110092 NA NA NA
## 173 0.038284839 0.012251149 NA NA NA
## 174 0.038284839 0.012251149 NA NA NA
## 175 0.038284839 0.012251149 NA NA NA
## 176 0.102446483 0.027522936 NA NA NA
## 177 0.114754098 0.023845007 NA NA NA
## 178 0.269113150 0.044342508 NA NA NA
## 179 0.240061162 0.032110092 NA NA NA
## 180 0.100917431 0.032110092 NA NA NA
## 181 0.100917431 0.032110092 NA NA NA
## 182 0.164885496 0.042748092 NA NA NA
## 183 0.164885496 0.042748092 NA NA NA
## 184 0.035114504 0.044274809 NA NA NA
## 185 0.035114504 0.044274809 NA NA NA
## 186 0.042748092 0.120610687 NA NA NA
## 187 0.042748092 0.120610687 NA NA NA
## 188 0.016793893 0.061068702 NA NA NA
## 189 0.113149847 0.025993884 NA NA NA
## 190 0.175840979 0.041284404 NA NA NA
## 191 0.175840979 0.041284404 NA NA NA
## 192 0.209160305 0.022900763 NA NA NA
## 193 0.209160305 0.022900763 NA NA NA
## 194 0.169465649 0.013740458 NA NA NA
## 195 0.169465649 0.013740458 NA NA NA
## 196 0.172782875 0.001529052 NA NA NA
## 197 0.172782875 0.001529052 NA NA NA
## 198 0.088820827 0.009188361 NA NA NA
## 199 0.088820827 0.009188361 NA NA NA
## 200 0.142201835 0.010703364 NA NA NA
## 201 0.142201835 0.010703364 NA NA NA
## 202 0.087155963 0.070336391 NA NA NA
## 203 0.074809160 0.056488550 NA NA NA
## 204 0.038226300 0.035168196 NA NA NA
## 205 0.032110092 0.007645260 NA NA NA
## 206 0.032110092 0.007645260 NA NA NA
## 207 0.302290076 0.035114504 NA NA NA
## 208 0.302290076 0.035114504 NA NA NA
## 209 0.302290076 0.035114504 NA NA NA
## 210 0.630534351 0.009160305 NA NA NA
## 211 0.630534351 0.009160305 NA NA NA
## 212 0.775229358 0.015290520 NA NA NA
## 213 0.775229358 0.015290520 NA NA NA
## 214 0.853211009 0.003058104 NA NA NA
## 215 0.853211009 0.003058104 NA NA NA
## 216 0.853211009 0.003058104 NA NA NA
## 217 0.835114504 0.000000000 NA NA NA
## 218 0.574923547 0.012232416 NA NA NA
## 219 0.574923547 0.012232416 NA NA NA
## 220 0.130168453 0.068912711 NA NA NA
## 221 0.062691131 0.079510703 NA NA NA
## 222 0.056574924 0.061162080 NA NA NA
## 223 0.022900763 0.140458015 NA NA NA
## 224 0.155963303 0.030581040 NA NA NA
## 225 0.155963303 0.030581040 NA NA NA
## 226 0.195718654 0.009174312 NA NA NA
## 227 0.195718654 0.009174312 NA NA NA
## 228 0.178625954 0.013740458 NA NA NA
## 229 0.250381679 0.004580153 NA NA NA
## 230 0.090214067 0.001529052 NA NA NA
## 231 0.042813456 0.010703364 NA NA NA
## 232 0.042813456 0.010703364 NA NA NA
## 233 0.108396947 0.045801527 NA NA NA
## 234 0.108396947 0.045801527 NA NA NA
## 235 0.053435115 0.331297710 NA NA NA
## 236 0.058103976 0.195718654 NA NA NA
## 237 0.044342508 0.003058104 NA NA NA
## 238 0.012213740 0.004580153 NA NA NA
## 239 0.012213740 0.004580153 NA NA NA
## 240 0.114754098 0.011922504 NA NA NA
## 241 0.114754098 0.011922504 NA NA NA
## 242 0.434250765 0.058103976 NA NA NA
## 243 0.434250765 0.058103976 NA NA NA
## 244 0.614678899 0.035168196 NA NA NA
## 245 0.711009174 0.007645260 NA NA NA
## 246 0.711009174 0.007645260 NA NA NA
## 247 0.871755725 0.007633588 NA NA NA
## 248 0.711009174 0.007645260 NA NA NA
## 249 0.397553517 0.029051988 NA NA NA
## 250 0.215267176 0.077862595 NA NA NA
## 251 0.305343511 0.032061069 NA NA NA
## 252 0.206422018 0.114678899 NA NA NA
## 253 0.001529052 0.081039755 NA NA NA
## 254 0.018320611 0.177099237 NA NA NA
## 255 0.018320611 0.177099237 NA NA NA
## 256 0.001526718 0.001526718 NA NA NA
## 257 0.065648855 0.001526718 NA NA NA
## 258 0.158778626 0.025954198 NA NA NA
## 259 0.158778626 0.025954198 NA NA NA
## 260 0.142201835 0.009174312 NA NA NA
## 261 0.166666667 0.004587156 NA NA NA
## 262 0.160305344 0.042748092 NA NA NA
## 263 0.135877863 0.007633588 NA NA NA
## 264 0.029051988 0.007645260 NA NA NA
## 265 0.050458716 0.016819572 NA NA NA
## 266 0.050458716 0.016819572 NA NA NA
## 267 0.221374046 0.184732824 NA NA NA
## 268 0.221374046 0.184732824 NA NA NA
## 269 0.051987768 0.675840979 NA NA NA
## 270 0.087155963 0.032110092 NA NA NA
## 271 0.087155963 0.032110092 NA NA NA
## 272 0.041284404 0.019877676 NA NA NA
## 273 0.041284404 0.019877676 NA NA NA
## 274 0.140458015 0.013740458 NA NA NA
## 275 0.036641221 0.091603053 NA NA NA
## 276 0.622324159 0.042813456 NA NA NA
## 277 0.613149847 0.007645260 NA NA NA
## 278 0.613149847 0.007645260 NA NA NA
## 279 0.676335878 0.003053435 NA NA NA
## 280 0.676335878 0.003053435 NA NA NA
## 281 0.827217125 0.001529052 NA NA NA
## 282 0.827217125 0.001529052 NA NA NA
## 283 0.827217125 0.001529052 NA NA NA
## 284 0.686544343 0.067278287 NA NA NA
## 285 0.613149847 0.006116208 NA NA NA
## 286 0.138931298 0.083969466 NA NA NA
## 287 0.598473282 0.024427481 NA NA NA
## 288 0.149847095 0.411314985 NA NA NA
## 289 0.061162080 0.131498471 NA NA NA
## 290 0.061162080 0.131498471 NA NA NA
## 291 0.018320611 0.038167939 NA NA NA
## 292 0.018320611 0.038167939 NA NA NA
## 293 0.007645260 0.021406728 NA NA NA
## 294 0.014028056 0.002004008 NA NA NA
## 295 0.163358779 0.019847328 NA NA NA
## 296 0.162079511 0.003058104 NA NA NA
## 297 0.097859327 0.013761468 NA NA NA
## 298 0.097709924 0.004580153 NA NA NA
## 299 0.076452599 0.038226300 NA NA NA
## 300 0.038226300 0.001529052 NA NA NA
## 301 0.042879020 0.010719755 NA NA NA
## 302 0.042879020 0.010719755 NA NA NA
## 303 0.042879020 0.010719755 NA NA NA
## 304 0.124042879 0.004594181 NA NA NA
## 305 0.124042879 0.004594181 NA NA NA
## 306 0.124042879 0.004594181 NA NA NA
## 307 0.079510703 0.154434251 NA NA NA
## 308 0.079510703 0.154434251 NA NA NA
## 309 0.133231240 0.088820827 NA NA NA
## 310 0.101071975 0.030627871 NA NA NA
## 311 0.101071975 0.030627871 NA NA NA
## 312 0.143730887 0.038226300 NA NA NA
## 313 0.090773810 0.026785714 NA NA NA
## 314 0.294027565 0.018376723 NA NA NA
## 315 0.473200613 0.007656968 NA NA NA
## 316 0.541284404 0.006116208 NA NA NA
## 317 0.541284404 0.006116208 NA NA NA
## 318 0.371559633 0.003058104 NA NA NA
## 319 0.371559633 0.003058104 NA NA NA
## 320 0.820826953 0.001531394 NA NA NA
## 321 0.820826953 0.001531394 NA NA NA
## 322 0.722817764 0.007656968 NA NA NA
## 323 0.722817764 0.007656968 NA NA NA
## 324 0.614678899 0.029051988 NA NA NA
## 325 0.795419847 0.013740458 NA NA NA
## 326 0.628440367 0.036697248 NA NA NA
## 327 0.469418960 0.059633028 NA NA NA
## 328 0.469418960 0.059633028 NA NA NA
## 329 0.178899083 0.126911315 NA NA NA
## 330 0.178899083 0.126911315 NA NA NA
## 331 0.108396947 0.099236641 NA NA NA
## 332 0.032061069 0.042748092 NA NA NA
## 333 0.003053435 0.006106870 NA NA NA
## 334 0.055130168 0.004594181 NA NA NA
## 335 0.181957187 0.015290520 NA NA NA
## 336 0.134762634 0.010719755 NA NA NA
## 337 0.036753446 0.010719755 NA NA NA
## 338 0.048929664 0.006116208 NA NA NA
## 339 0.042813456 0.024464832 NA NA NA
## 340 0.051987768 0.003058104 NA NA NA
## 341 0.051987768 0.003058104 NA NA NA
## 342 0.137614679 0.007645260 NA NA NA
## 343 0.137614679 0.007645260 NA NA NA
## 344 0.129770992 0.042748092 NA NA NA
## 345 0.129770992 0.042748092 NA NA NA
## 346 0.103975535 0.038226300 NA NA NA
## 347 0.103975535 0.038226300 NA NA NA
## 348 0.136085627 0.015290520 NA NA NA
## 349 0.136085627 0.015290520 NA NA NA
## 350 0.143511450 0.027480916 NA NA NA
## 351 0.071755725 0.030534351 NA NA NA
## 352 0.077862595 0.088549618 NA NA NA
## 353 0.321100917 0.010703364 NA NA NA
## 354 0.423547401 0.003058104 NA NA NA
## 355 0.423547401 0.003058104 NA NA NA
## 356 0.326717557 0.004580153 NA NA NA
## 357 0.398473282 0.004580153 NA NA NA
## 358 0.510703364 0.001529052 NA NA NA
## 359 0.510703364 0.001529052 NA NA NA
## 360 0.405198777 0.004587156 NA NA NA
## 361 0.405198777 0.004587156 NA NA NA
## 362 0.405198777 0.004587156 NA NA NA
## 363 0.426605505 0.001529052 NA NA NA
## 364 0.426605505 0.001529052 NA NA NA
## 365 0.774885145 0.007656968 NA NA NA
## 366 0.774885145 0.007656968 NA NA NA
## 367 0.618683002 0.007656968 NA NA NA
## 368 0.618683002 0.007656968 NA NA NA
## 369 0.476263400 0.021439510 NA NA NA
## 370 0.476263400 0.021439510 NA NA NA
## 371 0.498470948 0.076452599 NA NA NA
## 372 0.258805513 0.272588055 NA NA NA
## 373 0.050746269 0.062686567 NA NA NA
## 374 0.125382263 0.001529052 NA NA NA
## 375 0.140672783 0.001529052 NA NA NA
## 376 0.105504587 0.006116208 NA NA NA
## 377 0.056488550 0.022900763 NA NA NA
## 378 0.055045872 0.009174312 NA NA NA
## 379 0.045871560 0.003058104 NA NA NA
## 380 0.045871560 0.003058104 NA NA NA
## 381 0.128440367 0.001529052 NA NA NA
## 382 0.140672783 0.030581040 NA NA NA
## 383 0.138931298 0.093129771 NA NA NA
## 384 0.245801527 0.003053435 NA NA NA
## 385 0.245801527 0.003053435 NA NA NA
## 386 0.245801527 0.003053435 NA NA NA
## 387 0.567278287 0.000000000 NA NA NA
## 388 0.567278287 0.000000000 NA NA NA
## 389 0.119266055 0.029051988 NA NA NA
## 390 0.095238095 0.081845238 NA NA NA
## 391 0.259938838 0.024464832 NA NA NA
## 392 0.426605505 0.007645260 NA NA NA
## 393 0.426605505 0.007645260 NA NA NA
## 394 0.430534351 0.003053435 NA NA NA
## 395 0.429663609 0.018348624 NA NA NA
## 396 0.278287462 0.012232416 NA NA NA
## 397 0.260336907 0.024502297 NA NA NA
## 398 0.663608563 0.004587156 NA NA NA
## 399 0.663608563 0.004587156 NA NA NA
## 400 0.663608563 0.004587156 NA NA NA
## 401 0.745038168 0.025954198 NA NA NA
## 402 0.800000000 0.015267176 NA NA NA
## 403 0.800000000 0.015267176 NA NA NA
## 404 0.574923547 0.012232416 NA NA NA
## 405 0.319571865 0.042813456 NA NA NA
## 406 0.300763359 0.038167939 NA NA NA
## 407 0.538931298 0.045801527 NA NA NA
## 408 0.396946565 0.114503817 NA NA NA
## 409 0.000000000 0.000000000 NA NA NA
## 410 0.024464832 0.000000000 NA NA NA
## 411 0.019847328 0.003053435 NA NA NA
## 412 0.019847328 0.003053435 NA NA NA
## 413 0.134351145 0.048854962 NA NA NA
## 414 0.134351145 0.048854962 NA NA NA
## 415 0.103816794 0.067175573 NA NA NA
## 416 0.090076336 0.019847328 NA NA NA
## 417 0.061162080 0.036697248 NA NA NA
## 418 0.322629969 0.009174312 NA NA NA
## 419 0.322629969 0.009174312 NA NA NA
## 420 0.625382263 0.006116208 NA NA NA
## 421 0.625382263 0.006116208 NA NA NA
## 422 0.320061256 0.004594181 NA NA NA
## 423 0.320061256 0.004594181 NA NA NA
## 424 0.320061256 0.004594181 NA NA NA
## 425 0.209480122 0.059633028 NA NA NA
## 426 0.209480122 0.059633028 NA NA NA
## 427 0.272171254 0.004587156 NA NA NA
## 428 0.272171254 0.004587156 NA NA NA
## 429 0.171253823 0.013761468 NA NA NA
## 430 0.284403670 0.013761468 NA NA NA
## 431 0.505343511 0.006106870 NA NA NA
## 432 0.418320611 0.059541985 NA NA NA
## 433 0.422900763 0.001526718 NA NA NA
## 434 0.422900763 0.001526718 NA NA NA
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## 1117 POLYGON ((693969.5 6636588,...# ==============================================================================
# Bivariate map with cases, agriculture, and forest info
# ==============================================================================
# ref: https://timogrossenbacher.ch/2019/04/bivariate-maps-with-ggplot2-and-sf/
df = Hx # just for plotting
# create 3 buckets for forest
quantiles_forest <- HxDF %>%
pull(Forest) %>%
quantile(probs = seq(0, 1, length.out = 4))
# create 3 buckets for agriculture
quantiles_ag <- HxDF %>%
pull(Agricultural) %>%
quantile(probs = seq(0, 1, length.out = 4))
# create color scale that encodes two variables
# red for forest and blue for agriculture
bivariate_color_scale <- tibble(
"3 - 3" = "#3F2949",
"2 - 3" = "#435786",
"1 - 3" = "#4885C1",
"3 - 2" = "#77324C",
"2 - 2" = "#806A8A",
"1 - 2" = "#89A1C8",
"3 - 1" = "#AE3A4E",
"2 - 1" = "#BC7C8F",
"1 - 1" = "#CABED0"
) %>%
gather("group", "fill")
df %<>%
mutate(
forest_quantiles = cut(
Forest,
breaks = quantiles_forest,
include.lowest = TRUE
),
ag_quantiles = cut(
Agricultural,
breaks = quantiles_ag,
include.lowest = TRUE
),
group = paste(
as.numeric(forest_quantiles), "-",
as.numeric(ag_quantiles)
)
) %>%
left_join(bivariate_color_scale, by = "group")
map <- ggplot(data = df) +
geom_sf(aes(fill = fill),
color = "white",
size = 0.1) +
scale_fill_identity() +
geom_sf(data = TSp_sf, col = "yellow", size = 0.1, alpha = 0.5) +
theme_void()
# separate the groups
bivariate_color_scale %<>%
separate(group, into = c("forest", "ag"), sep = " - ") %>%
mutate(forest = as.integer(forest),
ag = as.integer(ag))
legend <- ggplot() +
geom_tile(
data = bivariate_color_scale,
mapping = aes(
x = forest,
y = ag,
fill = fill)
) +
scale_fill_identity() +
labs(x = "Higher forest → ",
y = "Higher ag →") +
theme(
axis.title = element_text(size = 6)
) +
coord_fixed()
ggdraw() +
draw_plot(map, 0, 0, 1, 1) +
draw_plot(legend, 0.05, 0.075, 0.2, 0.2, hjust = -3)
Interventions
Containtment barriers.
Barriers will be implemented as a rectangle whose diagonal is between points (773676.4, 6347189) and (833676.4, 6437189). We expect that the barriers will be active in 10 days (60 days after the detection of the index case).
# Use the diagonal points to create the square
fenceSp <- cbind(x = c(833676.4, 773676.4, 773676.4, 833676.4, 833676.4), y = c(6347189, 6347189, 6437189, 6437189, 6347189))
# Convert to polygon
fenceSp <- st_polygon(list(fenceSp)) %>%
st_geometry()
# Set the project CRS
st_crs(fenceSp) <- st_crs(Hx)
# SHow it in a map
Hx %>%
filter(!is.na(Farms)) %>%
ggplot() +
geom_sf() +
geom_sf(data = fenceSp, alpha = 0.3, col = 'red')
# export the shape:
fences_shp = list.files(here("Data/Period_1/out"),
pattern = "fenceSp", full.names = TRUE)
if(length(fences_shp) > 0) do.call(file.remove, list(fences_shp))## [1] TRUE TRUE TRUE TRUEfenceSp %>% st_write(., here("Data/Period_1/out/fenceSp.shp"))## Writing layer `fenceSp' to data source `/Users/nistara/projects/side/2020_ASFChallenge_UCD/Data/Period_1/out/fenceSp.shp' using driver `ESRI Shapefile'
## Writing 1 features with 0 fields and geometry type Polygon.Increased hunting pressure.
The goal is to kill about 90% of the alive wild boar within the fenced area, but to test 100%.